Cloud storage systems (Amazon S3, Dropbox, Google Drive etc.) are becoming increasingly popular as a means of storing data reliably and making it easily accessible from any location. Unfortunately, even though the remote storage provider may not be trusted, current systems provide few security or integrity guarantees.

Guaranteeing the privacy and authenticity of remotely stored data while allowing efficient access and updates is non-trivial and relates to the study of oblivious RAMs and memory checking, which we will return to later. The main focus of this work, however, is an orthogonal question: How can we efficiently verify that the entire client data are being stored on the remote server in the first place? In other words, what prevents the server from deleting some portion of the data (say, an infrequently accessed sector) to save on storage?

Provable Storage Motivated by the questions above, there has been much cryptography and security research in creating a provable storage mechanism, where an untrusted server can prove to a client that her data are kept intact. More precisely, the client can run an efficient audit protocol with the untrusted server, guaranteeing that the server can only pass the audit if it maintains full knowledge of the entire client data. This is formalized by requiring that the data can be efficiently extracted from the server given its state at the beginning of any successful audit. One may think of this as analogous to the notion of extractors in the definition of zero-knowledge proofs of knowledge [4, 17].

One trivial audit mechanism, which accomplishes the above, is for the client to simply download all of her data from the server and check its authenticity (e.g., using a MAC). However, for the sake of efficiency, we insist that the computation and communication of the server and client during an audit protocol are much smaller than the potentially huge size of the client’s data. In particular, the server should not even have to read all of the client’s data to run the audit protocol, let alone transmit it. A scheme that accomplishes the above is called a Proof of Retrievability (PoR).

Prior Techniques The first PoR schemes were defined and constructed by Juels and Kaliski [22] and have since received much attention. We review the prior work and closely related primitives (e.g., sublinear authenticators [26] and provable data possession [1]) in Sect. 1.2.

On a very high level, all PoR constructions share essentially the same common structure. The client stores some redundant encoding of her data under an erasure code on the server, ensuring that the server must delete a significant fraction of the encoding before losing any actual data. During an audit, the client then checks a few random locations of the encoding, so that a server who deleted a significant fraction will get caught with overwhelming probability.

More precisely, let us model the client’s input data as a string \(\mathbf{M }\in \Sigma ^\ell \) consisting of \(\ell \) symbols from some small alphabet \(\Sigma \), and let \({\textsf {Enc}}:\Sigma ^\ell \rightarrow \Sigma ^{\ell '}\) denote an erasure code that can correct the erasure of up to \(\frac{1}{2}\) of its output symbols. The client stores \({\textsf {Enc}}(\mathbf{M })\) on the server. During an audit, the client selects a small random subset of t out of the \(\ell '\) locations in the encoding and challenges the server to respond with the corresponding values, which it then checks for authenticity (e.g., using MAC tags). Intuitively, if the server deletes more than half of the values in the encoding, it will get caught with overwhelming probability \(> 1 - 2^{-t}\) during the audit, and otherwise it retains knowledge of the original data because of the redundancy of the encoding. The complexity of the audit protocol is only proportional to t which can be set to the security parameter and is independent of the size of the client data.Footnote 1

Difficulty of Updates One of the main limitations of all prior PoR schemes is that they do not support efficient updates to the client data. Under the above template for PoR, if the client wants to modify even a single location of \(\mathbf{M }\), it will end up needing to change the values of at least half of the locations in \({\textsf {Enc}}(\mathbf{M })\) on the server, requiring a large amount of work (linear in the size of the client data). Constructing a PoR scheme that allows for efficient updates was stated as the main open problem by Juels and Kaliski [22]. We emphasize that, in the setting of updates, the audit protocol must ensure that the server correctly maintains knowledge of the latest version of the client data, which includes all of the changes incurred over time. Before we describe our solution to this problem, let us build some intuition about the challenges involved by examining two natural but flawed proposals.

First Proposal A natural attempt to overcome the inefficiency of updating a huge redundant encoding is to encode the data “locally” so that a change to one position of the data only affects a small number of codeword symbols. More precisely, instead of using an erasure code that takes all \(\ell \) data symbols as input, we can use a code \({\textsf {Enc}}:\Sigma ^k\rightarrow \Sigma ^n\) that works on small blocks of only \(k\ll \ell \) symbols encoded into n symbols. The client divides the data \(\mathbf{M }\) into \(L = \ell /k\) message blocks \((\mathbf{m }_1,\ldots ,\mathbf{m }_L)\), where each block \(\mathbf{m }_i \in \Sigma ^k\) consists of k symbols. The client redundantly encodes each message block \(\mathbf{m }_i\) individually into a corresponding codeword block \(\mathbf{c }_i = {\textsf {Enc}}(\mathbf{m }_i)\in \Sigma ^n\) using the above code with small inputs. Finally the client concatenates these codeword blocks to form the value \(\mathbf{C }= (\mathbf{c }_1,\ldots ,\mathbf{c }_L) \in \Sigma ^{Ln}\), which it stores on the server. Auditing works as before: The client randomly chooses t of the \(L\cdot n\) locations in \(\mathbf{C }\) and challenges the server to respond with the corresponding codeword symbols in these locations, which it then tests for authenticity.Footnote 2 The client can now read/write to any location within her data by simply reading/writing to the n relevant codeword symbols on the server.

The above proposal can be made secure when the block size k (which determines the complexity of reads/updates) and the number of challenged locations t (which determines the complexity of the audit) are both set to \(\Omega (\sqrt{\ell })\) where \(\ell \) is the size of the data (see “Appendix 1” for details). This way, the audit is likely to check sufficiently many values in each codeword block \(\mathbf{c }_i\). Unfortunately, if we want a truly efficient scheme and set \(n,t = o(\sqrt{\ell })\) to be small, then this solution becomes completely insecure. The server can delete a single codeword block \(\mathbf{c }_i\) from \(\mathbf{C }\) entirely, losing the corresponding message block \(\mathbf{m }_i\), but still maintain a good chance of passing the above audit as long as none of the t random challenge locations coincides with the n deleted symbols, which happens with good probability.

Second Proposal The first proposal (with small nt) was insecure because a cheating server could easily identify the locations within \(\mathbf{C }\) that correspond to a single message block and delete exactly the codeword symbols in these locations. We can prevent such attacks by pseudorandomly permuting the locations of all of the different codeword symbols of different codeword blocks together. That is, the client starts with the value \(\mathbf{C }= (\mathbf{C }[1],\ldots ,\mathbf{C }[Ln]) = (\mathbf{c }_1,\ldots ,\mathbf{c }_L) \in \Sigma ^{Ln}\) computed as in the first proposal. It chooses a pseudorandom permutation \(\pi :~ [Ln] \rightarrow [Ln]\) and computes the permuted value \(\mathbf{C }' := (\mathbf{C }[\pi (1)], \ldots , \mathbf{C }[\pi (Ln)])\) which it then stores on the server in an encrypted form (each codeword symbol is encrypted separately). The audit still checks t out of Ln random locations of the server storage and verifies authenticity.

It may seem that the server now cannot immediately identify and selectively delete codeword symbols belonging to a single codeword block, thwarting the attack on the first proposal. Unfortunately, this modification only re-gains security in the static setting, when the client never performs any operations on the data.Footnote 3 Once the client wants to update some location of \(\mathbf{M }\) that falls inside some message block \(\mathbf{m }_i\), she has to reveal to the server where all of the n codeword symbols corresponding to \(\mathbf{c }_i = {\textsf {Enc}}(\mathbf{m }_i)\) reside in its storage since she needs to update exactly these values. Therefore, the server can later selectively delete exactly these n codeword symbols, leading to the same attack as in the first proposal.

Impossibility? Given the above failed attempts, it may even seem that truly efficient updates could be inherently incompatible with efficient audits in PoR. If an update is efficient and only changes a small subset of the server’s storage, then the server can always just ignore the update, thereby failing to maintain knowledge of the latest version of the client data. All of the prior techniques appear ineffective against such attack. More generally, any audit protocol which just checks a small subset of random locations of the server’s storage is unlikely to hit any of the locations involved in the update and hence will not detect such cheating, meaning that it cannot be secure.Footnote 4 However, this does not rule out the possibility of a very efficient solution that relies on a more clever audit protocol, which is likelier to check recently updated areas of the server’s storage and therefore detect such an attack. Indeed, this property will be an important component in our actual solution.

Our Results and Techniques

Overview of Result In this work, we give the first solution to dynamic PoR that allows for efficient updates to client data. The client only keeps some short local state and can execute arbitrary read/write operations on any location within the data by running a corresponding protocol with the server. At any point in time, the client can also initiate an audit protocol, which ensures that a passing server must have complete knowledge of the latest version of the client data. The cost of any read/write/audit execution in terms of server/client work and communication is only polylogarithmic in the size of the client data. The server’s storage remains linear in the size of the client data. Therefore, our scheme is optimal in an asymptotic sense, up to polylogarithmic factors. See Sect. 7 for a detailed efficiency analysis.

PoR Via Oblivious RAM Our dynamic PoR solution starts with the same idea as the first proposal above, where the client redundantly encodes small blocks of her data individually to form the value \(\mathbf{C }= (\mathbf{c }_1, \ldots , \mathbf{c }_L) \in \Sigma ^{Ln}\), consisting of L codeword blocks and \(\ell ' = Ln\) codeword symbols, as defined previously. The goal is to then store \(\mathbf{C }\) on the server in some “clever way” so that the server cannot selectively delete too many symbols within any single codeword block \(\mathbf{c }_i\), even after observing the client’s read and write executions (which access exactly these symbols). As highlighted by the second proposal, simply permuting the locations of the codeword symbols of \(\mathbf{C }\) is insufficient. Instead, our main idea is to store all of the individual codeword symbols of \(\mathbf{C }\) on the server using an oblivious RAM scheme.

Overview of ORAM Oblivious RAM (ORAM), initially defined by Goldreich and Ostrovsky [16], allows a client to outsource her memory to a remote server while allowing the client to perform random-access reads and writes in a private way. More precisely, the client has some data \(\mathbf{D }\in \Sigma ^d\), which she stores on the server in some carefully designed privacy-preserving form, while only keeping a short local state. She can later run efficient protocols with the server to read or write to the individual entries of \(\mathbf{D }\). The read/write protocols of the ORAM scheme should be efficient, and the client/server work and communication during each such protocol should be small compared to the size of \(\mathbf{D }\) (e.g., polylogarithmic). A secure ORAM scheme not only hides the content of \(\mathbf{D }\) from the server, but also the access pattern of which locations in \(\mathbf{D }\) the client is reading or writing in each protocol execution. Thus, the server cannot discern any correlation between the physical locations of its storage that it is asked to access during each read/write protocol execution and the logical location inside \(\mathbf{D }\) that the client wants to access via this protocol.

We review the literature and efficiency of ORAM schemes in Sect. 6. In our work, we will also always use ORAM schemes that are authenticated, which means that the client can detect if the server ever sends an incorrect value. In particular, authenticated ORAM schemes ensure that the most recent version of the data is being retrieved in any accepting read execution, preventing the server from “rolling back” updates.

Construction of Dynamic PoR A detailed technical description of our construction appears in Sect. 5, and below we give a simplified overview. In our PoR construction, the client starts with data \(\mathbf{M }\in \Sigma ^{\ell }\) which she splits into small message blocks \(\mathbf{M }= (\mathbf{m }_1, \ldots , \mathbf{m }_L)\) with \(\mathbf{m }_i \in \Sigma ^k\) where the block size \(k \ll \ell = Lk\) is only dependent on the security parameter. She then applies an error-correcting code \({\textsf {Enc}}:~ \Sigma ^k \rightarrow \Sigma ^n\) that can efficiently recover \(\frac{n}{2}\) erasures to each message block individually, resulting in the value \(\mathbf{C }= (\mathbf{c }_1, \ldots , \mathbf{c }_L) \in \Sigma ^{Ln}\) where \(\mathbf{c }_i = {\textsf {Enc}}(\mathbf{m }_i)\). Note that any constant fraction erasure-correcting code will work (i.e., \(\frac{n}{2}\) can be replaced with \(\frac{n}{s}\) for any constant s). Finally, she initializes an ORAM scheme with the initial data \(\mathbf{D }= \mathbf{C }\), which the ORAM stores on the server in some clever privacy-preserving form, while keeping only a short local state at the client.

Whenever the client wants to read or write to some location within her data, she uses the ORAM scheme to perform the necessary reads/writes on each of the n relevant codeword symbols of \(\mathbf{C }\) (see details in Sect. 5). To run an audit, the client chooses t (\(\approx \) security parameter) random locations in \(\{1,\ldots , Ln\}\) and runs the ORAM read protocol t times to read the corresponding symbols of \(\mathbf{C }\) that reside in these locations, checking them for authenticity.

Catching Disregarded Updates First, let us start with a sanity check, to explain how the above construction can thwart a specific attack in which the server simply disregards the latest update. In particular, such attack should be caught by a subsequent audit. During the audit, the client runs the ORAM protocol to read t random codeword symbols and these are unlikely to coincide with any of the n codeword symbols modified by the latest update (recall that t and n are both small and independent of the data size \(\ell \)). However, the ORAM scheme stores data on the server in a highly organized data structure and ensures that the most recently updated data are accessed during any subsequent “read” execution, even for an unrelated logical location. This is implied by ORAM security since we need to hide whether or not the location of a read was recently updated or not. Therefore, although the audit executes the “ORAM read” protocols on random logical locations inside \(\mathbf{C }\), the ORAM scheme will end up scanning recently updated areas of the server’s actual storage and check them for authenticity, ensuring that recent updates have not been disregarded.

Security and “Next-Read Pattern Hiding” The high-level security intuition for our PoR scheme is quite simple. The ORAM hides from the server where the various locations of \(\mathbf{C }\) reside in its storage, even after observing the access pattern of read/write executions. Therefore it is difficult for the server to reach a state where it will fail on read executions for most locations within some single codeword block (lose data) without also failing on too many read executions altogether (lose the ability to pass an audit).

Making the above intuition formal is quite subtle, and it turns out that standard notion of ORAM security does not suffice. The main issue is that the server may be able to somehow delete all (or most) of the n codeword symbols that fall within some codeword block \(\mathbf{c }_i = (\mathbf{C }[j +1], \ldots , \mathbf{C }[j+n])\) without knowing which block it deleted. Therefore, although the server will fail on any subsequent read if and only if its location falls within the range \(\{j+1, \ldots , j+n\}\), it will not learn anything about the location of the read itself since it does not know the index j. Indeed, we will give an example of a contrived ORAM scheme where such an attack is possible and our resulting construction of PoR using this ORAM is insecure.

We show, however, that the intuitive reasoning above can be salvaged if the ORAM scheme achieves a new notion of security that we call next-read pattern hiding (NRPH), which may be of independent interest. NRPH security considers an adversarial server that first gets to observe many read/write protocol executions performed sequentially with the client, resulting in some final client configuration \({\mathcal {C}}_{\textsf {fin}}\). The adversarial server then gets to see various possibilities for how the “next-read” operation would be executed by the client for various distinct locations, where each such execution starts from the same fixed client configuration \({\mathcal {C}}_{\textsf {fin}}\).Footnote 5 The server should not be able to discern any relationship between these executions and the locations they are reading. For example, two such “next-read” executions where the client reads two consecutive locations should be indistinguishable from two executions that read two random and unrelated locations. This notion of NRPH security will be used to show that server cannot reach a state where it can selectively fail to respond on read queries whose location falls within some small range of a single codeword block (lose data), but still respond correctly to most completely random reads (pass an audit).

Proving Security Via an Extractor As mentioned earlier, the security of PoR is formalized via an extractor and we now give a high-level overview of how such an extractor works. In particular, we claim that we can take any adversarial server that has a “good” chance of passing an audit and use the extractor to efficiently recover the latest version of the client data from it. The extractor initializes an “empty array” \(\mathbf{C }\). It then executes random audit protocols with the server by acting as the honest client. In particular, it chooses t random locations within the array and runs the corresponding ORAM read protocols. If the execution of the audit is successful, the extractor fills in the corresponding values of \(\mathbf{C }\) that it learned during the audit execution. In either case, it then rewinds the server and runs a fresh execution of the audit, repeating this step for several iterations.

Since the server has a good chance of passing a random audit, it is easy to show that the extractor can eventually recover a large fraction, say \(> \frac{3}{4}\), of the entries inside \(\mathbf{C }\) by repeating this process sufficiently many times. Because of the authenticity of the ORAM, the recovered values are the correct ones, corresponding to the latest version of the client data. Now we need to argue that there is no codeword block \(\mathbf{c }_i\) within \(\mathbf{C }\) for which the extractor recovered fewer than \(\frac{1}{2}\) of its codeword symbols, as this would prevent us from applying erasure decoding and recovering the underlying message block. Let FAILURE denote the above bad event. If all the recovered locations (comprising \(> \frac{3}{4}\) fraction of the total) were distributed uniformly within \(\mathbf{C }\), then FAILURE would occur with negligible probability, as long as the codeword size n is sufficiently large in the security parameter. Thus, intuitively, if the server does not perform a targeted attack, but randomly corrupt codeword blocks, FAILURE would not happen. We can now rely on the NRPH security of the ORAM to ensure that FAILURE also happens with negligible probability, even if the server tries to selectively corrupt codeword blocks. We can think of the FAILURE event as a function of the locations queried by the extractor in each audit execution and the set of executions on which the server fails. If the malicious server can cause FAILURE to occur, it means that it can distinguish the pattern of locations actually queried by the extractor during the audit executions (for which the FAILURE event occurs) from a randomly permuted pattern of locations (for which the FAILURE event does not occur with overwhelming probability). Therefore, a targeted attack by the server causing FAILURE with more than negligible probability means that the server can be used in a reduction to break the NRPH security. Note that the use of rewinding between the audit executions of the extractor requires us to rely on NRPH security rather than just standard ORAM security.

The above presents the high-level intuition and is somewhat oversimplified. See Sect. 4 for the formal definition of NRPH security and Sect. 5 for the formal description of our dynamic PoR scheme and a rigorous proof of security.

Achieving Next-Read Pattern Hiding We show that standard ORAM security does not generically imply NRPH security by giving a contrived scheme that satisfies the former but not the latter. Nevertheless, many natural ORAM constructions in the literature do seem to satisfy NRPH security. In particular, we examine the efficient ORAM construction of Goodrich and Mitzenmacher [18] and prove that (with minor modifications) it is NRPH secure.

Contributions We call our final scheme \(\textsf {{PORAM}}\) since it combines the techniques and security of PoR and ORAM. In particular, other than providing provable dynamic cloud storage as our main goal, our scheme also satisfies the strong privacy guarantees of ORAM, meaning that it hides all contents of the remotely stored data as well as the access pattern of which locations are accessed when. It also provides strong authenticity guarantees (same as memory checking; see Sect. 1.2), ensuring that any “read” execution with a malicious remote server is guaranteed to return the latest version of the data (or detect cheating).

In brief, our contributions can be summarized as follows:

  • We give the first asymptotically efficient solution to PoR for outsourced dynamic data, where a successful audit ensures that the server knows the latest version of the client data. In particular:

    • – Client storage is small and independent of the data size.

    • – Server storage is linear in the data size, expanding it by only a small constant factor.

    • – Communication and computation of client and server during read, write, and audit executions are polylogarithmic in the size of the client data.

  • Our scheme also achieves strong privacy and authenticity guarantees, matching those of oblivious RAM and memory checking.

  • We present a new security notion called “next-read pattern hiding (NRPH)” for ORAM and a construction achieving this new notion, which may be of independent interest.

We mention that the \(\textsf {{PORAM}}\) scheme is simple to implement and has low concrete efficiency overhead on top of an underlying ORAM scheme with NRPH security. There is much recent and ongoing research activity in instantiating/implementing truly practical ORAM schemes, which are likely to yield correspondingly practical instantiations of our \(\textsf {{PORAM}}\) protocol.

Related Work

Proofs of retrievability for static data were initially defined and constructed by Juels and Kaliski [22], building on a closely related notion called sublinear authenticators of Naor and Rothblum [26]. Concurrently, Ateniese et al.  [1] defined another related primitive called provable data possession (PDP). Since then, there has been much ongoing research activity on PoR and PDP schemes.

PoR Versus PDP The main difference between PoR and PDP is the notion of security that they achieve. A PoR audit guarantees that the server maintains knowledge of all of the client data, while a PDP audit only ensures that the server is storing most of the client data. For example, in a PDP scheme, the server may lose a small portion of client data (say 1 MB out of a 10 GB file) and may maintain an high chance of passing a future audit.Footnote 6 On a technical level, the main difference in most prior PDP/PoR constructions is that PoR schemes store a redundant encoding of the client data on the server. For a detailed comparison, see Küpçü [24, 25].

Static Data PoR and PDP schemes for static data (without updates) have received much research attention [2, 7, 12, 30], with works improving on communication efficiency and exact security, yielding essentially optimal solutions. Another interesting direction has been to extend these works to the multi-server setting [6, 10, 11] where the client can use the audit mechanism to identify faulty machines and recover the data from the others.

Dynamic Data The works of Ateniese et al.  [3], Erway et al.  [14] and Wang et al.  [35] show how to achieve PDP security for dynamic data, supporting efficient updates. This is closely related to work on memory checking [5, 13, 26], which studies how to authenticate remotely stored dynamic data so as to allow efficient reads/writes, while being able to verify the authenticity of the latest version of the data (preventing the server from “rolling back” updates and using an old version). Unfortunately, these techniques alone cannot be used to achieve the stronger notion of PoR security. Indeed, the main difficulty that we resolve in this work, how to efficiently update redundantly encoded data, does not come up in the context of PDP. Multi-server extensions of dynamic storage schemes exist as well [15].

A recent work of Stefanov et al.  [33] considers PoR for dynamic data, but in a more complex setting where an additional trusted “portal” performs some operations on behalf of the client and can cache updates for an extended period of time. It is not clear whether these techniques can be translated to the basic client/server setting, which we consider here. However, even in this modified setting, the complexity of the updates and the audit in that work is proportional to square root of the data size, whereas ours is polylogarithmic.


Notation Throughout, we use \(\lambda \) to denote the security parameter. We identify efficient algorithms as those running in (probabilistic) polynomial time in \(\lambda \) and their input lengths and identify negligible quantities (e.g., acceptable error probabilities) as \({\textsf {negl}}(\lambda ) = 1/\lambda ^{\omega (1)}\), meaning that they are asymptotically smaller than \(1/\lambda ^c\) for every constant \(c > 0\). For \(n \in {\mathbb {N}}\), we define the set \([n] \mathop {=}\limits ^\mathrm{def}\{1,\ldots ,n\}\). We use the notation \((k \mod ~ n)\) to denote the unique integer \(i \in \{0,\ldots ,n-1\}\) such that \(i = k ~ (\hbox {mod}~ n)\).

Erasure Codes We say that \(({\textsf {Enc}}, {\textsf {Dec}})\) is an \((n,k,d)_{\Sigma }\)-code with efficient erasure decoding over an alphabet \(\Sigma \) if the original message can always be recovered from a corrupted codeword with at most \(d-1\) erasures. That is, for every message \(\mathbf{m }= (m_1,\ldots ,m_k)\) \(\in \Sigma ^k\) giving a codeword \(\mathbf{c }= (c_1,\ldots ,c_n) = {\textsf {Enc}}(\mathbf{m })\), and every corrupted codeword \(\tilde{\mathbf{c }} = (\tilde{c}_1, \ldots , \tilde{c}_n)\) such that \(\tilde{c}_i \in \{c_i, \bot \}\) and the number of erasures is \( |\{ i \in [n]:~ \tilde{c}_i = \bot \}| \le d-1\), we have \({\textsf {Dec}}(\tilde{\mathbf{c }}) = \mathbf{m }\). We say that a code is systematic if, for every message \(\mathbf{m }\), the codeword \(\mathbf{c }= {\textsf {Enc}}(\mathbf{m })\) contains \(\mathbf{m }\) in the first k positions \(c_1 = m_1, \ldots , c_k = m_k\). A systematic variant of the Reed–Solomon code achieves the above for any integers \(n > k\) and any field \(\Sigma \) of size \(|\Sigma | \ge n\) with \(d = n - k + 1\).

Virtual Memory We think of virtual memory \(\mathbf{M }\), with word size w and length \(\ell \), as an array \(\mathbf{M }\in \Sigma ^\ell \) where \(\Sigma \mathop {=}\limits ^\mathrm{def}{\{0,1\}}^w\). We assume that, initially, each location \(\mathbf{M }[i]\) contains the special uninitialized symbol \(\mathbf{0 }= 0^w\). Throughout, we will think of \(\ell \) as some large polynomial in the security parameter, which upper bounds the amount of memory that can be used.

Outsourcing Virtual Memory In the next two sections, we look at two primitives: dynamic PoR and ORAM. These primitives allow a client to outsource some virtual memory \(\mathbf{M }\) of length \(\ell \) to a remote server, while providing useful security guarantees. Reading and writing to some location of \(\mathbf{M }\) now takes on the form of a protocol execution with the server. The goal is to provide security while preserving efficiency in terms of client/server computation, communication and the number of server memory accesses per operation, which should all be polylogarithmic in \(\ell \). We also want to optimize the size of the client storage (independent of \(\ell \)) and server storage (not much larger than \(\ell \)).

We find this abstract view of outsourcing memory to be the simplest and most general to work with. Any higher-level data structures and operations (e.g., allowing appends/inserts to data or implementing an entire file system) can be easily done on top of this abstract notion of memory and therefore securely outsourced to the remote server. Essentially, a file system employs a hard disk (which can be thought as the virtual memory \(\mathbf{M }\)) and then implements appropriate data structures to deal with directories, indexing and other operations. Just as the file system data structures reside on the hard disk itself, they all can be outsourced as part of our virtual memory abstraction, as if one is outsourcing the complete hard disk. Another alternative may be employing our system to outsource the actual files residing on the hard disk, but keeping the metadata or data structures at the local machine, since they generally require much lighter resources.

Dynamic PR

A Dynamic PoR scheme consists of protocols \(\mathbf{\textsf {P} }\textsf {Init}, \mathbf{\textsf {P} }\textsf {Read}, \mathbf{\textsf {P} }\textsf {Write},\) \({\textsf {Audit}}\) between two stateful parties: a client \({\mathcal {C}}\) and a server \({\mathcal {S}}\). The server acts as the curator for some virtual memory \(\mathbf{M }\), which the client can read, write and audit by initiating the corresponding interactive protocols:

  • \(\mathbf{\textsf {P} }\textsf {Init}(1^\lambda , 1^w , \ell )\): This protocol corresponds to the client initializing an (empty) virtual memory \(\mathbf{M }\) with word size w and length \(\ell \), which it supplies as inputs.

  • \(\mathbf{\textsf {P} }\textsf {Read}(i)\): This protocol corresponds to the client reading \(v = \mathbf{M }[i]\), where it supplies the input i and outputs some value v at the end.

  • \(\mathbf{\textsf {P} }\textsf {Write}(i,v)\): This protocol corresponds to setting \(\mathbf{M }[i] := v\), where the client supplies the inputs iv.

  • \({\textsf {Audit}}\): This protocol is used by the client to verify that the server is maintaining the memory contents correctly so that they remain retrievable. The client outputs a decision \(b \in \{ \mathtt{accept }, \mathtt{reject }\}\).

The client \({\mathcal {C}}\) in the protocols may be randomized, but we assume (w.l.o.g.) that the honest server \({\mathcal {S}}\) is deterministic. At the conclusion of the \(\mathbf{\textsf {P} }\textsf {Init}\) protocol, both the client and the server create some long-term local state, which each party will update during the execution of each of the subsequent protocols. The client may also output \(\mathtt{reject }\) during the execution of the \(\mathbf{\textsf {P} }\textsf {Init}, \mathbf{\textsf {P} }\textsf {Read}, \mathbf{\textsf {P} }\textsf {Write}\) protocols, to denote that it detected some misbehavior of the server. Note that we assume that the virtual memory is initially empty, but if the client has some initial data, she can write it onto the server block by block immediately after initialization. For ease of presentation, we may assume that the state of the client and the server always contains the security parameter and the memory parameters \((1^\lambda , 1^w , \ell )\).

We now define the three properties of a dynamic PoR scheme: correctness, authenticity and retrievability. For these definitions, we say that \(P = (op_0, op_1,\ldots ,op_q)\) is a dynamic PoR protocol sequence if \(op_0 = \mathbf{\textsf {P} }\textsf {Init}(1^\lambda , 1^w, \ell )\) and, for \(j >0\), \(op_j \in \{\mathbf{\textsf {P} }\textsf {Read}(i)\), \(\mathbf{\textsf {P} }\textsf {Write}(i,v)\), \({\textsf {Audit}}\}\) for some index \(i \in [\ell ]\) and value \(v \in {\{0,1\}}^w\).

Correctness If the client and the server are both honest and \(P = (op_0,\ldots ,op_q)\) is some protocol sequence, then we require the following to occur with probability 1 over the randomness of the client:

  • Each execution of a protocol \(op_j = \mathbf{\textsf {P} }\textsf {Read}(i)\) results in the client outputting the correct value \(v = \mathbf{M }[i]\), matching what would happen if the corresponding operations were performed directly on a memory \(\mathbf{M }\). More formally, if \(op_{j'} = \mathbf{\textsf {P} }\textsf {Write}(i,v)\) was the last \(\mathbf{\textsf {P} }\textsf {Write}\) operation on location i with \(j' < j\), then \(op_j = \mathbf{\textsf {P} }\textsf {Read}(i)\) returns v. If no prior \(\mathbf{\textsf {P} }\textsf {Write}\) operation on location i exists, then \(op_j = \mathbf{\textsf {P} }\textsf {Read}(i)\) returns \(\mathbf{0 }\) (the initial value).

  • Each execution of the \({\textsf {Audit}}\) protocol results in the decision \(b = \mathtt{accept }\).

Authenticity We require that the client can always detect if any protocol message sent by the server deviates from honest behavior. More precisely, consider the following game \(\mathtt{AuthGame }_{{\tilde{{\mathcal {S}}}}}(\lambda )\) between a malicious server \({\tilde{{\mathcal {S}}}}\) and a challenger:

  • The malicious server \({\tilde{{\mathcal {S}}}}(1^\lambda )\) specifies a valid protocol sequence \(P = (op_0,\ldots ,op_q)\).

  • The challenger initializes a copy of the honest client \({\mathcal {C}}\) and the (deterministic) honest server \({\mathcal {S}}\). It sequentially executes \(op_0,\ldots ,op_q\) between \({\mathcal {C}}\) and the malicious server \({\tilde{{\mathcal {S}}}}\) while, in parallel, also passing a copy of every message from \({\mathcal {C}}\) to the honest server \({\mathcal {S}}\).

  • If, at any point during the execution of some \(op_j\), any protocol message given by \({\tilde{{\mathcal {S}}}}\) differs from that of \({\mathcal {S}}\), and the client \({\mathcal {C}}\) does not output \(\mathtt{reject }\), the adversary wins and the game outputs 1. Else 0.

For any efficient adversarial server \({\tilde{{\mathcal {S}}}}\), we require \(\Pr [\mathtt{AuthGame }_{{\tilde{{\mathcal {S}}}}}(\lambda ) = 1] \le {\textsf {negl}}(\lambda ).\) Note that authenticity and correctness together imply that the client will always either read the correct value corresponding to the latest contents of the virtual memory or reject whenever interacting with a malicious server.

Retrievability Finally we define the main purpose of a dynamic PoR scheme, which is to ensure that the client data remain retrievable. We wish to guarantee that whenever the malicious server is in a state with a reasonable probability \(\delta \) of successfully passing an audit, he must know the entire content of the client’s virtual memory \(\mathbf{M }\). As in “proofs of knowledge,” we formalize knowledge via the existence of an efficient extractor \({\mathcal {E}}\) which can recover the value \(\mathbf{M }\) given (black-box) access to the malicious server.

More precisely, we define the game \(\mathtt ExtGame _{{\tilde{{\mathcal {S}}}}, {\mathcal {E}}}(\lambda , p)\) between a malicious server \({\tilde{{\mathcal {S}}}}\), extractor \({\mathcal {E}}\) and challenger:

  • The malicious server \({\tilde{{\mathcal {S}}}}(1^\lambda )\) specifies a protocol sequence \(P = (op_0,\ldots ,op_q)\). Let \(\mathbf{M }\in \Sigma ^\ell \) be the correct value of the memory contents at the end of honestly executing P.

  • The challenger initializes a copy of the honest client \({\mathcal {C}}\) and sequentially executes \(op_0,\ldots ,op_q\) between \({\mathcal {C}}\) and \({\tilde{{\mathcal {S}}}}\). Let \({\mathcal {C}}_{\textsf {fin}}\) and \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}\) be the final configurations (states) of the client and malicious server at the end of this interaction, including all of the random coins of the malicious server. Define the success probability

    $$\begin{aligned} \mathbf{\textsf {Succ} }(\tilde{{\mathcal {S}}}_{{\textsf {fin}}}) \mathop {=}\limits ^\mathrm{def}\Pr \left[ \tilde{{\mathcal {S}}}_{{\textsf {fin}}} \mathop {\longleftrightarrow }\limits ^{{\textsf {Audit}}}{\mathcal {C}}_{{\textsf {fin}}} = \mathtt{accept }\right] \end{aligned}$$

    as the probability that an execution of a subsequent \({\textsf {Audit}}\) protocol between \(\tilde{{\mathcal {S}}}_{{\textsf {fin}}}\) and \({\mathcal {C}}_{{\textsf {fin}}}\) results in the latter outputting \(\mathtt{accept }\). The probability is only over the random coins of \({\mathcal {C}}_{{\textsf {fin}}}\) during this execution.

  • Run \(\mathbf{M }' \leftarrow {\mathcal {E}}^{{\tilde{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^{\ell } , 1^{p})\), where the extractor \({\mathcal {E}}\) gets black-box rewinding access to the malicious server in its final configuration \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}\) and attempts to extract out the memory contents as \(\mathbf{M }'\).Footnote 7

  • If \(\mathbf{\textsf {Succ} }({\tilde{{\mathcal {S}}}}_{\textsf {fin}}) \ge 1/p\) and \(\mathbf{M }' \ne \mathbf{M }\) then output 1, else 0.

We require that there exists a probabilistic-poly-time extractor \({\mathcal {E}}\) such that, for every efficient malicious server \({\tilde{{\mathcal {S}}}}\) and every polynomial \(p = p(\lambda )\), we have \(\Pr [\mathtt ExtGame _{{\tilde{{\mathcal {S}}}}, {\mathcal {E}}}(\lambda , p) =1] \le {\textsf {negl}}(\lambda ).\)

The above says that whenever the malicious server reaches some state \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}\) in which it maintains a \(\delta \ge 1/p\) probability of passing the next audit, the extractor \({\mathcal {E}}\) will be able to extract out the correct memory contents \(\mathbf{M }\) from \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}\), meaning that the server must retain full knowledge of \(\mathbf{M }\) in this state. The extractor is efficient, but can run in time polynomial in p and the size of the memory \(\ell \).

A Note on Adaptivity We defined the above authenticity and retrievability properties assuming that the sequence of read/write operations is adversarial, but is chosen non-adaptively, before the adversarial server sees any protocol executions. Even though an adaptive security definition is preferable (and matches previous work in dynamic provable data possession setting [14]), standard ORAM security definitions in prior works have been non-adaptive. Thus, to be consistent with the ORAM literature, we have presented non-adaptive versions of the definitions above. Nevertheless, we note that our final results also achieve adaptive security, where the attacker can choose the sequence of operations \(op_i\) adaptively after seeing the execution of previous operations, if the underlying ORAM satisfies adaptive security (the proof remains exactly the same). Indeed, most prior ORAM solutions seem to achieve adaptive security, but it was never included in their analysis. Below we will sketch why one ORAM construction is adaptively secure (using its original proof) and also why our construction works without modification.

Oblivious RAM with Next-Read Pattern Hiding

An ORAM consists of protocols \(({\mathbf{\textsf {O} }\textsf {Init}}, {\mathbf{\textsf {O} }\textsf {Read}}, {\mathbf{\textsf {O} }\textsf {Write}})\) between a client \({\mathcal {C}}\) and a server \({\mathcal {S}}\), with the same syntax as the corresponding protocols in PoR. We will also extend the syntax of \(\mathbf{\textsf {O} }\textsf {Read}\) and \(\mathbf{\textsf {O} }\textsf {Write}\) to allow for reading/writing from/to multiple distinct locations simultaneously. That is, for arbitrary \(t \in {\mathbb {N}}\), we define the protocol \(\mathbf{\textsf {O} }\textsf {Read}(i_1,\ldots ,i_t)\) for distinct indices \(i_1,\ldots ,i_t \in [\ell ]\), in which the client outputs \((v_1,\ldots ,v_t)\) corresponding to reading \(v_1 = \mathbf{M }[i_1], \ldots , v_t = \mathbf{M }[i_t]\). Similarly, we define the protocol \(\mathbf{\textsf {O} }\textsf {Write}(i_t,\ldots ,i_t; v_1, \ldots , v_t)\) for distinct indices \(i_1,\ldots ,i_t \in [\ell ]\), which corresponds to setting \(\mathbf{M }[i_1] := v_1,\ldots ,\) \(\mathbf{M }[i_t] := v_t\).

We say that \(P = (op_0, \ldots , op_q)\) is an ORAM protocol sequence if \(op_0 = {\mathbf{\textsf {O} }\textsf {Init}}(1^\lambda , 1^w, \ell )\) and, for \(j > 0\), \(op_j\) is a valid (multi-location) read/write operation.

We require that an ORAM construction needs to satisfy correctness and authenticity, which are defined the same way as in PoR.Footnote 8 For privacy, we define a new property called next-read pattern hiding. For completeness, we also define the standard notion of ORAM pattern hiding in Appendix “2.”

Next-Read Pattern Hiding Consider an honest-but-curious server \({\mathcal {A}}\) who observes the execution of some protocol sequence P with a client \({\mathcal {C}}\) resulting in the final client configuration \({\mathcal {C}}_{\textsf {fin}}\). At the end of this execution, \({\mathcal {A}}\) gets to observe how \({\mathcal {C}}_{\textsf {fin}}\) would execute the next read operation \(\mathbf{\textsf {O} }\textsf {Read}(i_1,\ldots , i_t)\) for various different t-tuples \((i_1,\ldots ,i_t)\) of locations, but always starting in the same client state \({\mathcal {C}}_{\textsf {fin}}\). We require that \({\mathcal {A}}\) cannot observe any correlation between these next-read executions and their locations up to equality. That is, \({\mathcal {A}}\) should not be able to distinguish if \({\mathcal {C}}_{\textsf {fin}}\) instead executes the next-read operations on permuted locations \(\mathbf{\textsf {O} }\textsf {Read}(\pi (i_1),\ldots , \pi (i_t))\) for a permutation \(\pi :~ [\ell ] \rightarrow [\ell ]\).

More formally, we define \(\mathtt{NextReadGame }^b_{{\mathcal {A}}}(\lambda )\), for \(b \in {\{0,1\}}\), between an adversary \({\mathcal {A}}\) and a challenger:

  • The attacker \({\mathcal {A}}(1^\lambda )\) chooses an ORAM protocol sequence \(P_1 = (op_0,\ldots ,op_{q_1})\). It also chooses a sequence \(P_2 = \) \((rop_1,\) \(\ldots , rop_{q_2})\) of valid multi-location read operations, where each operation is of the form \(rop_j =\) \( \mathbf{\textsf {O} }\textsf {Read}(i_{j,1},\ldots ,i_{j,t_j})\) with \(t_j\) distinct locations. Lastly, it chooses a permutation \(\pi :~ [\ell ] \rightarrow [\ell ]\). For each \(rop_j\) in \(P_2\), define a permuted version \(rop'_j := \mathbf{\textsf {O} }\textsf {Read}(\pi (i_{j,1}), \ldots , \pi (i_{j,t_j}))\). The game now proceeds in two stages.

  • Stage I. The challenger initializes the honest client \({\mathcal {C}}\) and the (deterministic) honest server \({\mathcal {S}}\). It sequentially executes the protocols \(P_1 = (op_0, \ldots , op_{q_1})\) between \({\mathcal {C}}\) and \({\mathcal {S}}\). Let \({\mathcal {C}}_{\textsf {fin}}, {\mathcal {S}}_{\textsf {fin}}\) be the final configuration of the client and server at the end.

  • Stage II. For each \(j \in [q_2]\): Challenger either executes the original operation \(rop_j\) if \(b=0\), or the permuted operation \(rop'_j\) if \(b=1\), between \({\mathcal {C}}\) and \({\mathcal {S}}\). At the end of each operation execution, it resets the configuration of the client and server back to \({\mathcal {C}}_{\textsf {fin}}, {\mathcal {S}}_{\textsf {fin}}\), respectively, before the next execution.

  • The adversary \({\mathcal {A}}\) is given the transcript of all the protocol executions in stages I and II and outputs a bit \(\tilde{b}\) which we define as the output of the game. Note that since the honest server \({\mathcal {S}}\) is deterministic, seeing the protocol transcripts between \({\mathcal {S}}\) and \({\mathcal {C}}\) is the same as seeing the entire internal state of \({\mathcal {S}}\) at any point of time.

We require that, for every efficient \({\mathcal {A}}\), we have

$$\begin{aligned} \left| \Pr [\mathtt{NextReadGame }^0_{{\mathcal {A}}}(\lambda ) = 1] - \Pr [\mathtt{NextReadGame }^1_{{\mathcal {A}}}(\lambda ) = 1]\right| \le {\textsf {negl}}(\lambda ). \end{aligned}$$

Adaptive Security As mentioned at the end of Sect. 2, the definition of ORAM security used here and the standard definition in “Appendix 2” are non-adaptive, meaning the protocol sequence is declared at the beginning of the experiment. A stronger adaptive definition would allow the adversary to choose the sequence one operation at a time, potentially depending on its view up to that point.

Specifically, the adaptive version of next-read pattern hiding allows the adversary to select the protocol sequence \(P_1\) one operation at a time, where the next operation is selected after the completion of the prior one. At some point, the adversary declares that it is moving to the next stage and selects all of \(P_2\). The rest of the definition is left unchanged.

PORAM: Dynamic PoR via ORAM

We now give our construction of dynamic PoR, using ORAM. Since the ORAM security properties are preserved by the construction as well, we happen to achieve ORAM and dynamic PoR simultaneously. Therefore, we call our construction \(\textsf {{PORAM}}\).

Overview of Construction Let \(({\textsf {Enc}},\) \({\textsf {Dec}})\) be an \((n,k,d=n-k+1)_{\Sigma }\) systematic code with efficient erasure decoding over the alphabet \(\Sigma = {\{0,1\}}^w\) (e.g., the systematic Reed–Solomon code over \({\mathbb {F}}_{2^w}\)). Our construction of dynamic PoR will interpret the memory \(\mathbf{M }\in \Sigma ^\ell \) as consisting of \(L = \ell /k\) consecutive message blocks, each having k alphabet symbols (assume k is small and divides \(\ell \)). The construction implicitly maps operations on \(\mathbf{M }\) to operations on encoded memory \(\mathbf{C }\in (\Sigma )^{\ell _{{\textsf {code}}}= Ln}\), which consists of L codeword blocks with n alphabet symbols each. The L codeword blocks \(\mathbf{C }= (\mathbf{c }_1,\ldots ,\mathbf{c }_L)\) are simply the encoded versions of the corresponding message blocks in \(\mathbf{M }= (\mathbf{m }_1,\ldots ,\mathbf{m }_L)\) with \(\mathbf{c }_q = {\textsf {Enc}}(\mathbf{m }_q)\) for \(q \in [L]\). This means that, for each \(i \in [\ell ]\), the value of the memory location \(\mathbf{M }[i]\) can only affect the values of the encoded memory locations \(\mathbf{C }[j+1],\ldots ,\mathbf{C }[j+n]\) where \(j = n \cdot \lfloor i/k \rfloor \). Furthermore, since the encoding is systematic, we have \(\mathbf{M }[i] = \mathbf{C }[j + u]\) where \(u = (i ~\mod ~ k) + 1\). To read the memory location \(\mathbf{M }[i]\), the client will use ORAM to read the codeword location \(\mathbf{C }[j + u]\). To write to the memory location \(\mathbf{M }[i] := v\), the client needs to update the entire corresponding codeword block. She does so by first using ORAM to read the corresponding codeword block \(\mathbf{c }= (\mathbf{C }[j+1],\ldots ,\mathbf{C }[j+n])\) and decodes to obtain the original memory block \(\mathbf{m }= {\textsf {Dec}}(\mathbf{c })\).Footnote 9 She then locally updates the memory block by setting \(\mathbf{m }[u] := v\), re-encodes the updated memory block to get \(\mathbf{c }' = (c_1, \ldots , c_n) := {\textsf {Enc}}(\mathbf{m })\) and uses the ORAM to write \(\mathbf{c }'\) back into the encoded memory, setting \(\mathbf{C }[j+1] := c'_1, \ldots , \mathbf{C }[j+n]:= c'_n\).

The Construction Our \(\textsf {{PORAM}}\) construction is defined for some parameters \(n > k, t \in {\mathbb {N}}\). Let \(\mathbf{\textsf {O} }= ({\mathbf{\textsf {O} }\textsf {Init}}, {\mathbf{\textsf {O} }\textsf {Read}}, \mathbf{\textsf {O} }\textsf {Write})\) be an ORAM. Let \(({\textsf {Enc}},\) \({\textsf {Dec}})\) be an \((n,k,d=n-k+1)_{\Sigma }\) systematic code with efficient erasure decoding over the alphabet \(\Sigma = {\{0,1\}}^w\) (e.g., the systematic Reed–Solomon code over \({\mathbb {F}}_{2^w}\)).

  • \(\mathbf{\textsf {P} }\textsf {Init}(1^\lambda , 1^w, \ell )\):   Assume k divides \(\ell \) and let \(\ell _{{\textsf {code}}}:= n \cdot (\ell /k) \). Run the \(\mathbf{\textsf {O} }\textsf {Init}(1^\lambda , 1^w, \ell _{{\textsf {code}}})\) protocol.

  • \(\mathbf{\textsf {P} }\textsf {Read}(i)\):   Let \(i' := n \cdot \lfloor i/k \rfloor + (i~\mod k) + 1\) and run the \(\mathbf{\textsf {O} }\textsf {Read}(i')\) protocol.

  • \(\mathbf{\textsf {P} }\textsf {Write}(i,v)\):   Set \(j := n \cdot \lfloor i/k \rfloor \) and \(u:= (i ~\mod ~ k) + 1\).

    • - Run \(\mathbf{\textsf {O} }\textsf {Read}(j+1, \ldots , j+n)\) and get output \(\mathbf{c }= (c_1,\ldots ,c_n)\).

    • - Decode \(\mathbf{m }= (m_1,\ldots ,m_k) = {\textsf {Dec}}(\mathbf{c })\).

    • - Modify position u of \(\mathbf{m }\) by locally setting \(m_u := v\). Re-encode the modified message block \(\mathbf{m }\) by setting \(\mathbf{c }' = (c'_1,\ldots ,c'_n) := {\textsf {Enc}}(\mathbf{m })\).

    • - Run \(\mathbf{\textsf {O} }\textsf {Write}(j+1, \ldots , j+ n; c'_1,\ldots ,c'_n)\).

  • \({\textsf {Audit}}\):   Pick t distinct indices \(j_1,\ldots ,j_t \in [\ell _{{\textsf {code}}}]\) at random. Run \(\mathbf{\textsf {O} }\textsf {Read}(j_1,\ldots ,j_t)\) and return \(\mathtt{accept }\) iff the protocol finished without outputting \(\mathtt{reject }\).

If any ORAM protocol execution in the above scheme outputs \(\mathtt{reject }\), the client enters a special rejection state in which it stops responding and automatically outputs \(\mathtt{reject }\) for any subsequent protocol execution.

It is easy to see that if the underlying ORAM scheme used in the above \(\textsf {{PORAM}}\) construction is secure in the standard sense of ORAM (see “Appendix 2”), then the above construction preserves this ORAM security, hiding which locations are being accessed in each operation. As our main result, we now prove that if the ORAM scheme satisfies next-read pattern hiding (NRPH) security, then the \(\textsf {{PORAM}}\) construction above is also a secure dynamic PoR scheme.

Theorem 1

Assume that \(\mathbf{\textsf {O} }= (\mathbf{\textsf {O} }\textsf {Init}, \mathbf{\textsf {O} }\textsf {Read}, \mathbf{\textsf {O} }\textsf {Write})\) is an ORAM with next-read pattern hiding (NRPH) security, and we choose parameters \(k = \Omega (\lambda ),~~ k/n = (1 - \Omega (1)),~~ t = \Omega (\lambda )\). Then the above scheme \(\textsf {{PORAM}}= (\mathbf{\textsf {P} }\textsf {Init}, \mathbf{\textsf {P} }\textsf {Read}, \mathbf{\textsf {P} }\textsf {Write}, {\textsf {Audit}})\) is a dynamic PoR scheme.

Proof of Theorem 1

The correctness and authenticity properties of \(\textsf {{PORAM}}\) follow immediately from those of the underlying ORAM scheme \(\mathbf{\textsf {O} }\). The main challenge is to show that the retrievability property holds. As a first step, let us describe the extractor.

The Extractor The extractor \({\mathcal {E}}^{{\tilde{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^\ell , 1^p)\) works as follows:

  1. (1)

    Initialize \(\mathbf{C }:= (\bot )^{\ell _{{\textsf {code}}}}\) where \(\ell _{{\textsf {code}}}= n(\ell /k)\) to be an empty vector.

  2. (2)

    Keep rewinding and auditing the server by repeating the following step for \(s = \max (2\ell _{{\textsf {code}}}, \lambda )\cdot p\) times: Pick t distinct indices \(j_1,\ldots , j_t \in [\ell _{{\textsf {code}}}]\) at random and run the protocol \(\mathbf{\textsf {O} }\textsf {Read}(j_1,\ldots ,j_t)\) with \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}\), acting as \({\mathcal {C}}_{\textsf {fin}}\) as in the audit protocol. If the protocol is accepting and \({\mathcal {C}}_{\textsf {fin}}\) outputs \((v_1,\ldots ,v_t)\), set \(\mathbf{C }[j_1] := v_1, \ldots , \mathbf{C }[j_t] := v_t\). Rewind \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}, {\mathcal {C}}_{\textsf {fin}}\) to their state prior to this execution for the next iteration.

  3. (3)

    Let \(\delta \mathop {=}\limits ^\mathrm{def}(1 + \frac{k}{n})/2\). If the number of “filled-in” values in \(\mathbf{C }\) is \(|\{j \in [\ell _{{\textsf {code}}}]:~ \mathbf{C }[j] \ne \bot \}| < \delta \cdot \ell _{{\textsf {code}}}\), then output \({\textsf {fail}}_1\). Else interpret \(\mathbf{C }\) as consisting of \(L = \ell /k\) consecutive codeword blocks \(\mathbf{C }= (\mathbf{c }_1, \ldots , \mathbf{c }_L)\) with each block \(\mathbf{c }_j \in \Sigma ^n\). If there exists some index \(j \in [L]\) such that the number of“filled-in” values in codeword block \(\mathbf{c }_j\) is \(|\{ i \in [n]:~ \mathbf{c }_j[i] \ne \bot \}| < k \) then output \({\textsf {fail}}_2\). Otherwise, apply erasure decoding to each codeword block \(\mathbf{c }_j\), to recover \(\mathbf{m }_j = {\textsf {Dec}}(\mathbf{c }_j)\), and output \(\mathbf{M }= (\mathbf{m }_1,\ldots ,\mathbf{m }_L) \in \Sigma ^{\ell }\).Footnote 10

Sequence of Hybrids Let \({\tilde{{\mathcal {S}}}}\) be a PPT adversarial server and \(p = p(\lambda )\) be some polynomial. Our goal is to prove the following:

$$\begin{aligned} \Pr [\mathtt ExtGame _{{\tilde{{\mathcal {S}}}}, {\mathcal {E}}}(\lambda , p(\lambda )) =1] \le {\textsf {negl}}(\lambda ) \end{aligned}$$

where \(\mathtt ExtGame _{{\tilde{{\mathcal {S}}}}, {\mathcal {E}}}(\lambda , p(\lambda ))\) defined in Sect. 3.

We will prove this via a sequence of hybrid games called \({\textsf {Hybrid}}~i\) for \(i=0,\ldots ,5\) where \({\textsf {Hybrid}}~0\) is defined as \(\mathtt ExtGame _{{\tilde{{\mathcal {S}}}}, {\mathcal {E}}}(\lambda , p(\lambda ))\). We will show that for all \(i < 5\):

$$\begin{aligned} \Pr [ {\textsf {Hybrid}}~i \hbox { outputs } 1] \le \Pr [ {\textsf {Hybrid}}~i+1 \hbox { outputs } 1] + {\textsf {negl}}(\lambda ) \end{aligned}$$

and \(\Pr [{\textsf {Hybrid}}~5 \hbox { outputs } 1] \le {\textsf {negl}}(\lambda )\). This suffices to prove the theorem. Intuitively, in each hybrid we will make that changes that appear to make it less likely that the game outputs 1, but then we will show that we did not make it significantly less likely then previously. The hybrids all have the same high-level structure as \(\mathtt ExtGame _{{\tilde{{\mathcal {S}}}}, {\mathcal {E}}}(\lambda , p(\lambda ))\) except that we will make modifications to the definition of the winning condition that causes the game to output 1, the code of the extractor \({\mathcal {E}}\), and the adversary \({\tilde{{\mathcal {S}}}}\). Starting with the original game, we first show that the failures are rare events. Then we show that we can use an estimated success probability and replace the active attacker with a passive one, without noticeable differences. Lastly, we define a “permuted extractor” and conclude our proof with an information theoretic argument.

\({\textsf {Hybrid}}~0\) (the original game): This is the \(\mathtt ExtGame _{{\tilde{{\mathcal {S}}}}, {\mathcal {E}}}(\lambda , p(\lambda ))\). Using the same notation as in the definition of \(\mathtt ExtGame \), let \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}, {\mathcal {C}}_{\textsf {fin}}\) be the final configurations of the malicious server \({\tilde{{\mathcal {S}}}}\) and client \({\mathcal {C}}\), respectively, after executing the protocol sequence P chosen by the server at the beginning of the game, and let \(\mathbf{M }\) be the correct value of the memory contents resulting from P. The output of the game is 1 iff

$$\begin{aligned} \mathbf{\textsf {Succ} }(\tilde{{\mathcal {S}}}_{{\textsf {fin}}}) > \frac{1}{p(\lambda )} ~~~\wedge ~~~ {\mathcal {E}}^{{\tilde{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^{\ell } , 1^{p}) \ne \mathbf{M }\end{aligned}$$

\({\textsf {Hybrid}}~1\) (extractor fails with \({\textsf {fail}}_1,{\textsf {fail}}_2\)): This game is the same as \({\textsf {Hybrid}}~0\) except that we define its output to be 1 iff

$$\begin{aligned} \mathbf{\textsf {Succ} }(\tilde{{\mathcal {S}}}_{{\textsf {fin}}}) > \frac{1}{p(\lambda )} ~~~\wedge ~~~ {\mathcal {E}}^{{\tilde{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^{\ell } , 1^{p}) \in \{{\textsf {fail}}_1,{\textsf {fail}}_2\}. \end{aligned}$$

In other words, we do not count the event that the extractor outputs some non-fail incorrect value \(\mathbf{M }' \ne \mathbf{M }\) in the winning probability.

Note that the hybrids 0 and 1 only differ in their output if \({\mathcal {E}}^{{\tilde{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^{\ell } , 1^{p}) \not \in \{\mathbf{M }, {\textsf {fail}}_1,{\textsf {fail}}_2\}\). Therefore, to show that \(\Pr [ {\textsf {Hybrid}}~0 \hbox { outputs } 1] \le \Pr [ {\textsf {Hybrid}}~ 1 \hbox { outputs } 1] + {\textsf {negl}}(\lambda )\), it suffices to show the following.

Lemma 1

Within the execution of \(\mathtt ExtGame _{{\tilde{{\mathcal {S}}}}, {\mathcal {E}}}(\lambda , p)\), we have:

$$\begin{aligned} \Pr [ {\mathcal {E}}^{{\tilde{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^{\ell } , 1^{p}) \not \in \{\mathbf{M }, {\textsf {fail}}_1,{\textsf {fail}}_2\}] \le {\textsf {negl}}(\lambda ). \end{aligned}$$

Proof of Lemma

The only way that the above bad event can occur is if the extractor puts an incorrect value into its array \(\mathbf{C }\) which does not match encoded version of the correct memory contents \(\mathbf{M }\). In particular, this means that one of the audit protocol executions (consisting of an \(\mathbf{\textsf {O} }\textsf {Read}\) with t random locations) initiated by the extractor \({\mathcal {E}}\) between the malicious server \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}\) and the client \({\mathcal {C}}_{\textsf {fin}}\) causes the client to output some incorrect value which does not match correct memory contents \(\mathbf{M }\) and not reject. By the correctness of the ORAM scheme, this means that the the malicious server must have deviated from honest behavior during that protocol execution, without the client rejecting. Assume the probability of this bad event happening is \(\rho \). Since the extractor runs \(s = \max (2\ell _{{\textsf {code}}}, \lambda )\cdot p = {\textsf {poly}}(\lambda )\) such protocol executions with rewinding, there is at least \(\rho /s = \rho /{\textsf {poly}}(\lambda )\) probability that the above bad event occurs on a single random execution of the audit with \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}\). But this means that \({\tilde{{\mathcal {S}}}}\) can be used to break the authenticity of ORAM with advantage \(\rho /{\textsf {poly}}(\lambda )\), by first running the requested protocol sequence P and then deviating from honest behavior during a subsequent \(\mathbf{\textsf {O} }\textsf {Read}\) protocol without being detected. Therefore, by the authenticity of ORAM, we must have \(\rho = {\textsf {negl}}(\lambda )\).\(\square \)

\({\textsf {Hybrid}}~2\) (extractor fails with \({\textsf {fail}}_2\)): This game is the same as \({\textsf {Hybrid}}~1\) except that we define its output to be 1 iff

$$\begin{aligned} \mathbf{\textsf {Succ} }(\tilde{{\mathcal {S}}}_{{\textsf {fin}}}) > \frac{1}{p(\lambda )} ~~~\wedge ~~~ {\mathcal {E}}^{{\tilde{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^{\ell } , 1^{p}) = {\textsf {fail}}_2. \end{aligned}$$

In other words, we do not count the event that the extractor fails with \({\textsf {fail}}_1\) in the winning probability.

Note that the hybrids 1 and 2 only differ in their output if \(\mathbf{\textsf {Succ} }(\tilde{{\mathcal {S}}}_{{\textsf {fin}}}) > \frac{1}{p(\lambda )} \wedge {\mathcal {E}}^{{\tilde{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^{\ell } , 1^{p}) = {\textsf {fail}}_1\). Therefore, to show that \(\Pr [ {\textsf {Hybrid}}~1 \hbox { outputs } 1] \le \Pr [ {\textsf {Hybrid}}~ 2 \hbox { outputs } 1] + {\textsf {negl}}(\lambda )\), it suffices to show the following lemma which intuitively says that if \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}\) has a good chance of passing an audit, then the extractor must be able to extract sufficiently many values inside \(\mathbf{C }\) and hence cannot output \({\textsf {fail}}_1\). Remember that \({\textsf {fail}}_1\) occurs if the extractor does not have enough values to recover the whole memory, and \({\textsf {fail}}_2\) occurs if the extractor does not have enough values to recover some message block.

Lemma 2

For any (even inefficient) machine \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}\) and any polynomial \(p = p(\lambda )\) such that \(\mathbf{\textsf {Succ} }(\tilde{{\mathcal {S}}}_{{\textsf {fin}}} ) \ge 1/p\), we have

$$\begin{aligned} \Pr [{\mathcal {E}}^{\tilde{{\mathcal {S}}}_{{\textsf {fin}}}}({\mathcal {C}}_{\textsf {fin}}, 1^\ell , 1^p) = {\textsf {fail}}_1] \le {\textsf {negl}}(\lambda ). \end{aligned}$$

Proof of Lemma

Let E be the bad event that \({\textsf {fail}}_1\) occurs. For each iteration \(i \in [s]\) within step (2) of the execution of \({\mathcal {E}}\), let us define:

  • \(X_i\) to be an indicator random variable that takes on the value \(X_i = 1\) iff the \(\mathbf{\textsf {O} }\textsf {Read}\) protocol execution in iteration i does not reject.

  • \(G_i\) to be a random variable that denotes the subset \(\{j \in [\ell _{{\textsf {code}}}]:~ \mathbf{C }[j] \ne \bot \}\) of filled-in positions in the current version of \(\mathbf{C }\) at the beginning of iteration i.

  • \(Y_i\) to be an indicator random variable that takes on the value \(Y_i = 1\) iff \(|G_i| < \delta \cdot \ell _{{\textsf {code}}}\) and all of the locations that \({\mathcal {E}}\) chooses to read in iteration i happen to satisfy \(j_1,\ldots ,j_t \in G_i\).

If \(X_i = 1\) and \(Y_i = 0\) in iteration i, then at least one position of \(\mathbf{C }\) gets filled in so \(|G_{i+1}| \ge |G_i| + 1\). Therefore the bad event E only occurs if fewer than \(\delta \ell _{{\textsf {code}}}\) of the \(X_i\) takes on a 1 or at least one \(Y_i\) takes on a 1, giving us:

$$\begin{aligned} \Pr [E]\le & {} \Pr \left[ \sum _{i=1}^s X_i < \delta \ell _{{\textsf {code}}}\right] + \sum _{i=1}^s\Pr [Y_i = 1] \end{aligned}$$

For each i, we can bound \(\Pr [Y_i = 1] \le {\lfloor \delta \ell _{{\textsf {code}}}\rfloor \atopwithdelims ()t}/{\ell _{{\textsf {code}}}\atopwithdelims ()t} \le \delta ^t\). If we define \(\overline{X} = \frac{1}{s}\sum _{i=1}^s X_i\), we also get:

$$\begin{aligned} \Pr \left[ \sum _{i=1}^s X_i < \delta \ell _{{\textsf {code}}}\right]\le & {} \Pr \left[ \overline{X} < 1/p - \left( 1/p - \frac{\delta \ell _{{\textsf {code}}}}{s}\right) \right] \\\le & {} \exp (-2s(1/p - \delta \ell _{{\textsf {code}}}/s)^2)\\\le & {} \exp (-s/p) \le 2^{-\lambda } \end{aligned}$$

where the second inequality follows by the Chernoff–Hoeffding bound. Therefore \(\Pr [E] \le 2^{-\lambda } + s\delta ^t = {\textsf {negl}}(\lambda )\) which proves the lemma.\(\square \)

\({\textsf {Hybrid}}~3\) (uses estimated success probability): This game is the same as \({\textsf {Hybrid}}~2\) except that we redefine when the game outputs 1 once again. Instead of looking at the true success probability \(\mathbf{\textsf {Succ} }({\tilde{{\mathcal {S}}}}_{\textsf {fin}})\), which we cannot efficiently compute, we instead consider an estimated probability \({\widetilde{\mathbf{\textsf {Succ} }}}({\tilde{{\mathcal {S}}}}_{\textsf {fin}})\) which is computed in the context of \(\mathtt ExtGame \) by sampling \(2 \lambda (p(\lambda ))^2\) different “audit protocol executions” between \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}\) and \({\mathcal {C}}_{\textsf {fin}}\) and seeing on which fraction of them does \({\tilde{{\mathcal {S}}}}\) succeed (while rewinding \({\tilde{{\mathcal {S}}}}_{\textsf {fin}}\) and \({\mathcal {C}}_{\textsf {fin}}\) after each one). We define \({\textsf {Hybrid}}~3\) to output 1 iff:

$$\begin{aligned} {\widetilde{\mathbf{\textsf {Succ} }}}(\tilde{{\mathcal {S}}}_{{\textsf {fin}}}) > \frac{1}{2p(\lambda )} ~~~\wedge ~~~ {\mathcal {E}}^{{\tilde{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^{\ell } , 1^{p}) = {\textsf {fail}}_2. \end{aligned}$$

Note that there are two changes here: (1) We use the estimated rather than true success probability, and (2) we only require that the estimated success probability is at least \(\frac{1}{2p(\lambda )}\) whereas previously we wanted the true success probability to be at least \(\frac{1}{p(\lambda )}\).

To show that \(\Pr [ {\textsf {Hybrid}}~2 \hbox { outputs } 1] \le \Pr [ {\textsf {Hybrid}}~ 3 \hbox { outputs } 1] + {\textsf {negl}}(\lambda )\), we rely on the Chernoff–Hoeffding bound, which tells us that the estimated success probability is close to the real. In particular, whenever \(\mathbf{\textsf {Succ} }({\tilde{{\mathcal {S}}}}_{\textsf {fin}}) > \frac{1}{p(\lambda )}\) holds, we have: \(\Pr \left[ {\widetilde{\mathbf{\textsf {Succ} }}}({\tilde{{\mathcal {S}}}}_{\textsf {fin}}) \le \frac{1}{2p(\lambda )}\right] \le e^{-\lambda } = {\textsf {negl}}(\lambda ).\)

\({\textsf {Hybrid}}~4\) (passive attacker): In this hybrid, we replace the active attacker \({\tilde{{\mathcal {S}}}}\) with a passive attacker \({\hat{{\mathcal {S}}}}\) who always acts as the honest server \({\mathcal {S}}\), but can selectively fail by outputting \(\bot \) at any point. In particular \({\hat{{\mathcal {S}}}}\) just runs a copy of \({\tilde{{\mathcal {S}}}}\) and the honest server \({\mathcal {S}}\) concurrently, and if the outputs of \({\tilde{{\mathcal {S}}}}\) ever deviates from the execution of \({\mathcal {S}}\), it just outputs \(\bot \). As in \({\textsf {Hybrid}}~3\), the game outputs 1 iff

$$\begin{aligned} {\widetilde{\mathbf{\textsf {Succ} }}}({\hat{{\mathcal {S}}}}_{{\textsf {fin}}}) > \frac{1}{2p(\lambda )} ~~~\wedge ~~~ {\mathcal {E}}^{{\hat{{\mathcal {S}}}}}({\mathcal {C}}_{\textsf {fin}}, 1^{\ell } , 1^{p}) = {\textsf {fail}}_2. \end{aligned}$$

We argue that \(\Pr [ {\textsf {Hybrid}}~3 \hbox { outputs } 1] \le \Pr [ {\textsf {Hybrid}}~ 4 \hbox { outputs } 1] + {\textsf {negl}}(\lambda )\). The only difference between \({\textsf {Hybrid}}~3\) and \({\textsf {Hybrid}}~4\) is if \({\textsf {Hybrid}}~3\) the adversary \({\tilde{{\mathcal {S}}}}\) deviates from the protocol execution without being detected by the client (resulting in the client outputting \(\bot \)), either during the protocol execution of P or during one of the polynomially many executions of the next read used to compute \({\widetilde{\mathbf{\textsf {Succ} }}}({\tilde{{\mathcal {S}}}})\) and \({\mathcal {E}}^{{\tilde{{\mathcal {S}}}}}\). The probability that this occurs is negligible by authenticity of ORAM.

\({\textsf {Hybrid}}~5\) (permuted extractor): In this hybrid, we replace the extractor \({\mathcal {E}}\) with a “permuted extractor” \({\mathcal {E}}_{{\textsf {perm}}}\) who works just like \({\mathcal {E}}\) with the exception that it permutes the locations used in the \(\mathbf{\textsf {O} }\textsf {Read}\) executions during the extraction process. In particular, \({\mathcal {E}}_{\textsf {perm}}\) makes the following modifications to \({\mathcal {E}}\):

  • At the beginning, \({\mathcal {E}}_{{\textsf {perm}}}\) chooses a random permutation \(\pi :~ [\ell _{{\textsf {code}}}] \rightarrow [\ell _{{\textsf {code}}}]\).

  • During each of the s iterations of the audit protocol, \({\mathcal {E}}_{\textsf {perm}}\) chooses t indices \(j_1,\ldots , j_t \in [\ell _{{\textsf {code}}}]\) at random as before, but it then runs \(\mathbf{\textsf {O} }\textsf {Read}(\pi (j_1),\ldots ,\pi (j_t))\) on the permuted values. If the protocol is accepting, the extractor \({\mathcal {E}}_{\textsf {perm}}\) still “fills in” the original locations: \(\mathbf{C }[j_1], \ldots , \mathbf{C }[j_t]\). (Since we are only analyzing the event \({\textsf {fail}}_2\), we do not care about the values in these locations but only if they are filled in or not).

As in \({\textsf {Hybrid}}~4\), the game outputs 1 iff

$$\begin{aligned} {\widetilde{\mathbf{\textsf {Succ} }}}({\hat{{\mathcal {S}}}}_{{\textsf {fin}}}) > \frac{1}{2p(\lambda )} ~~~\wedge ~~~ {\mathcal {E}}_{{\textsf {perm}}}^{{\hat{{\mathcal {S}}}}}({\mathcal {C}}_{\textsf {fin}}, 1^{\ell } , 1^{p}) = {\textsf {fail}}_2. \end{aligned}$$

We argue that the two hybrids are indistinguishable in the “next-read pattern hiding” of the ORAM. In particular, this guarantees that permuting the locations inside of the \(\mathbf{\textsf {O} }\textsf {Read}\) executions (with rewinding) is indistinguishable.

Lemma 3

\(\Pr [ {\textsf {Hybrid}}~4 \hbox { outputs } 1] \le \Pr [ {\textsf {Hybrid}}~ 5 \hbox { outputs } 1] + {\textsf {negl}}(\lambda )\).

Proof of Lemma

We construct a reduction which converts an adversarial server \({\hat{{\mathcal {S}}}}\) against the PORAM scheme into an adversary \({\mathcal {A}}\) in the “next-read pattern hiding game” \(\mathtt{NextReadGame }^b_{\mathcal {A}}(\lambda )\) against the ORAM.

The adversary \({\mathcal {A}}\) runs \({\hat{{\mathcal {S}}}}\) who chooses a PoR protocol sequence \(P_1 = (op_0,\ldots ,op_{q_2})\), and \({\mathcal {A}}\) translates this to the appropriate ORAM protocol sequence, as defined by the \(\textsf {{PORAM}}\) scheme. Then \({\mathcal {A}}\) chooses its own sequence \(P_2 = (rop_1, \ldots , rop_{q_2})\) of sufficiently many read operations \(\mathbf{\textsf {O} }\textsf {Read}(i_1,\ldots ,i_t)\) where \(i_1,\ldots ,i_t \in [\ell _{{\textsf {code}}}]\) are random distinct indices. It then passes \(P_1, P_2\) to its challenger and gets back the transcripts of the protocol executions for stages (I) and (II) of the game.

The adversary \({\mathcal {A}}\) then uses the client communication from the stage (I) transcript to run \({\hat{{\mathcal {S}}}}\), getting it into some state \({\hat{{\mathcal {S}}}}_{\textsf {fin}}\). It then uses the stage (II) transcripts, to compute \({\mathcal {E}}^{{\hat{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^\ell , 1^p) \mathop {=}\limits ^{{{\tiny {?}}}}{\textsf {fail}}_2\) and to estimate \({\widetilde{\mathbf{\textsf {Succ} }}}({\hat{{\mathcal {S}}}}_{{\textsf {fin}}})\), without knowing the client state \({\mathcal {C}}_{\textsf {fin}}\). It does so just by checking on which executions does \({\hat{{\mathcal {S}}}}_{\textsf {fin}}\) abort with \(\bot \) and on which it runs to completion (here we use that \({\hat{{\mathcal {S}}}}\) is semi-honest and never deviates beyond outputting \(\bot \)). Lastly \({\mathcal {A}}\) outputs 1 iff the emulated extraction \({\mathcal {E}}^{{\hat{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^\ell , 1^p) = {\textsf {fail}}_2\) and \({\widetilde{\mathbf{\textsf {Succ} }}}({\hat{{\mathcal {S}}}}_{\textsf {fin}}) \ge \frac{1}{2p(\lambda )}\).

Let b be the challenger’s bit in the “next-read pattern hiding game.” If \(b=0\) (not permuted), then \({\mathcal {A}}\) perfectly emulates the distribution of \({\mathcal {E}}^{{\hat{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^\ell , 1^p) \mathop {=}\limits ^{{{\tiny {?}}}}{\textsf {fail}}_2\) and the estimation of \({\widetilde{\mathbf{\textsf {Succ} }}}({\hat{{\mathcal {S}}}}_{{\textsf {fin}}})\) so:

$$\begin{aligned} \Pr [\mathtt{NextReadGame }^0_{{\mathcal {A}}}(\lambda ) = 1] = \Pr [ {\textsf {Hybrid}}~4 \hbox { outputs } 1]. \end{aligned}$$

If \(b =1\) (permuted) then \({\mathcal {A}}\) perfectly emulates the distribution of the permuted extractor \({\mathcal {E}}_{\textsf {perm}}^{{\hat{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^\ell , 1^p) \mathop {=}\limits ^{{{\tiny {?}}}}{\textsf {fail}}_2\) and the estimation of \({\widetilde{\mathbf{\textsf {Succ} }}}({\hat{{\mathcal {S}}}}_{{\textsf {fin}}})\) since, for the latter, it does not matter whether random reads are permuted or not. Therefore:

$$\begin{aligned} \Pr [\mathtt{NextReadGame }^1_{{\mathcal {A}}}(\lambda ) = 1] = \Pr [ {\textsf {Hybrid}}~5 \hbox { outputs } 1]. \end{aligned}$$

The lemma follows since, by the next-read pattern hiding security, we know that \(|\Pr [\mathtt{NextReadGame }^0_{{\mathcal {A}}}(\lambda ) = 1] - \Pr [\mathtt{NextReadGame }^1_{{\mathcal {A}}}(\lambda ) = 1] \le {\textsf {negl}}(\lambda )\).\(\square \)

Probability \({\textsf {Hybrid}}~5\) Outputs 1 is Negligible. Finally, we present an information theoretic argument that the probability that \({\textsf {Hybrid}}~5\) outputs 1 is negligible.

Lemma 4

We have

$$\begin{aligned} \Pr [ {\textsf {Hybrid}}~ 5 \hbox { outputs } 1]= & {} \Pr \left[ \mathbf{\textsf {Succ} }(\tilde{{\tilde{{\mathcal {S}}}}}_{{\textsf {fin}}}) > \frac{1}{2p(\lambda )} \wedge {\mathcal {E}}_{{\textsf {perm}}}^{{\tilde{{\mathcal {S}}}}_{\textsf {fin}}}({\mathcal {C}}_{\textsf {fin}}, 1^{\ell } , 1^{p}) = {\textsf {fail}}_2 \right] \\\le & {} {\textsf {negl}}(\lambda ). \end{aligned}$$

Proof of Lemma

Firstly, note that an equivalent way of thinking about \({\mathcal {E}}_{\textsf {perm}}\) is to have it issue random (unpermuted) read queries just like \({\mathcal {E}}\) to recover \(\mathbf{C }\), but then permute the locations of \(\mathbf{C }\) via some permutation \(\pi {:}~ [\ell _{{\textsf {code}}}] \rightarrow [\ell _{{\textsf {code}}}]\) before testing for the event \({\textsf {fail}}_2\). This is simply because we have the distributional equivalence \((\pi (\mathtt{random }), \mathtt{random }) \equiv (\mathtt{random }, \pi (\mathtt{random }))\), where \(\mathtt{random }\) represents the randomly chosen locations for the audit and \(\pi \) is a random permutation. Now, with this interpretation of \({\mathcal {E}}_{\textsf {perm}}\), the event \({\textsf {fail}}_2\) occurs only if (I) the unpermuted \(\mathbf{C }\) contains more than \(\delta \) fraction of locations with filled-in (non \(\bot \)) values so that \({\textsf {fail}}_1\) does not occur, and (II) the permuted version \((\mathbf{c }_1, \ldots , \mathbf{c }_L) = \mathbf{C }[\pi (1)], \ldots ,\mathbf{C }[\pi (\ell _{{\textsf {code}}})]\) contains some codeword block \(\mathbf{c }_j\) with fewer than k / n fraction of filled-in (non \(\bot \)) values.

We now show that conditioned on (I) the probability of (II) is negligible over the random choice of \(\pi \). Fix some index \(j \in [L]\) and let us bound the probability that \(\mathbf{c }_j\) is the “bad” codeword block with fewer than k filled-in values. Let \(X_1, X_2, \ldots , X_n\) be random variables where \(X_i\) is 1 if \(\mathbf{c }_j[i] \ne \bot \) and 0 otherwise. Let \(\overline{X} \mathop {=}\limits ^\mathrm{def}\frac{1}{n}\sum _{i=1}^n X_i\). Then, over the randomness of \(\pi \), the random variables \(X_1, \ldots , X_n\) are sampled without replacement from a population of \(\ell _{{\textsf {code}}}\) values (location in \(\mathbf{C }\)) at least \(\delta \ell _{{\textsf {code}}}\) of which are 1 (\(\ne \bot \)) and the rest are 0 (\(=\bot \)). Therefore, by Hoeffding’s bound for sampling from finite populations without replacement (See Sect. 6 of [21]), we have:

$$\begin{aligned} \Pr [\mathbf{c }_j \text { is bad }]= & {} \Pr [\overline{X} < k/n] =\Pr [\overline{X} < \delta - (\delta - k/n)]\\\le & {} \exp (-2n(\delta - k/n)^2) = {\textsf {negl}}(\lambda ) \end{aligned}$$

By taking a union bound over all codeword blocks \(\mathbf{c }_j\), we can bound

$$\begin{aligned} \Pr [\exists _j{:}~ \mathbf{c }_j \text { is bad }] \le \sum _{j=1}^{\ell /k} \Pr [\mathbf{c }_j \text { is bad }] \le {\textsf {negl}}(\lambda ). \end{aligned}$$

This proves the lemma.\(\square \)

Together with the sequence of hybrids and the above lemma, we get

$$\begin{aligned} \Pr [\mathtt ExtGame _{{\tilde{{\mathcal {S}}}}, {\mathcal {E}}}(\lambda , p(\lambda )) =1]= & {} \Pr [ {\textsf {Hybrid}}~ 0 \hbox { outputs } 1] \le \Pr [ {\textsf {Hybrid}}~ 5 \hbox { outputs } 1]\\&+\, {\textsf {negl}}(\lambda ) \le {\textsf {negl}}(\lambda ) \end{aligned}$$

which proves the theorem.

Adaptive Security Note that the above proof goes through with essentially no changes for the adaptive definition of dynamic PoR security as long as the underlying ORAM achieves adaptive security. To show Lemma 1, we now need to rely on the fact that ORAM provides authenticity even in the adaptive setting. To prove Lemma 3, we now need to rely on the fact that ORAM provides NRPH security even in the adaptive setting. In particular, the only change is that in the proof of Lemma 3, the dynamic PoR adversary \({\hat{{\mathcal {S}}}}\) can choose the operations in the sequence \(P_1\) adaptively, and the NRPH adversary \({\mathcal {A}}\) therefore needs to translate these operations one by one into the corresponding adaptive sequence of ORAM operations.

ORAM Instantiation

The notion of ORAM was introduced by Goldreich and Ostrovsky [16], who also introduced the so-called hierarchical scheme having the structure seen in Figs. 1 and 2. Since then, several improvements to the hierarchical scheme have been given, including improved rebuild phases and the use of advanced hashing techniques [18, 29, 36].

Fig. 1
figure 1

Our Construction

Fig. 2
figure 2

Server data structures in the ORAM instantiation

We examine a particular ORAM scheme of Goodrich and Mitzenmacher [18] and show that (with minor modifications) it satisfies next-read pattern hiding security. Therefore, this scheme can be used to instantiate our \(\textsf {{PORAM}}\) construction. We note that most other ORAM schemes from the literature that follow the hierarchical structure also seemingly satisfy next-read pattern hiding, and we only focus on the above example for concreteness. However, in Appendix “3,” we show that it is not the case that every ORAM scheme satisfies next-read pattern hiding and in fact give an example of a contrived scheme which does not satisfy this notion and makes our construction of \(\textsf {{PORAM}}\) completely insecure. We also believe that there are natural schemes, such as the ORAM of Shi et al. [31], which do not satisfy this notion. Therefore, next-read pattern hiding is a meaningful property beyond standard ORAM security and must be examined carefully.

Overview We note that ORAM schemes are generally not described as protocols, but simply as a data structure in which the client’s encrypted data are stored on the server. Each time that a client wants to perform a read or write to some address i of her memory, this operation is translated into a series of read/write operations on this data structure inside the server’s storage. In other words, the (honest) server does not perform any computation at all during these “protocols,” but simply allows the client to access arbitrary locations inside this data structure.

Most ORAM schemes, including the one we will use below, follow a hierarchical structure. They maintain several levels of hash tables on the server, each holding encrypted address–value pairs, with lower tables having higher capacity. The tables are managed so that the most recently accessed data are kept in the top tables and the least recently used data are kept in the bottom tables. Over time, infrequently accessed data are moved into lower tables (obliviously).

To write a value to some address, just insert the encrypted address–value pair in the top table. To read the value at some address, one hashes the address and checks the appropriate position in the top table. If it is found in that table, then one hides this fact by sequentially checking random positions in the remaining tables. If it is not found in the top table, then one hashes the address again and checks the second-level table, continuing down the list until it is found and then accessing random positions in the remaining tables. Once all of the tables have been accessed, the found data are written into the top table. To prevent tables from overflowing (due to too many item insertions), there are additional periodic rebuild phases which obliviously move data from the smaller tables to larger tables further down.

Security Intuition The reason that we always write found data into the top table after any read is to protect the privacy of repeatedly reading the same address, ensuring that this looks the same as reading various different addresses. In particular, reading the same address twice will not need to access the same locations on the server, since after the first read, the data will already reside in the top table, and the random locations will be read at lower tables.

At any point in time, after the server observes many read/write executions, any subsequent read operation just accesses completely random locations in each table, from the point of view of the server. This is the main observation needed to argue standard pattern hiding. For next-read pattern hiding, we notice that we can extend the above to any set of q distinct executions of a subsequent read operation with distinct addresses (each execution starting in the same client/server state). In particular, each of the q operations just accesses completely random locations in each table, independently of the other operations, from the point of view of the server.

One subtlety comes up when the addresses are not completely distinct from each other, as is the case in our definition where each address can appear in multiple separate multi-read operations. The issue is that doing a read operation on the same address twice with rewinding will reveal the level at which the data for that address is stored, thus revealing some information about which address is being accessed. One can simply observe at which level do the accesses begin to differ in the two executions. We fix this issue by modifying a scheme so that, instead of accessing freshly chosen random positions in lower tables once the correct value is found, we instead access pseudorandom positions that are determined by the address being read and the operation count. That way, any two executions which read the same address starting from the same client state are exactly the same and do not reveal anything beyond this. Note that, without state rewinds, this still provides regular pattern hiding.

Technical Tools

Our construction uses the standard notion of a pseudorandom function (PRF) where F(Kx) denote the evaluation of the PRF F on input x with key K. We also rely on a symmetric-key encryption scheme secure against chosen-plaintext attacks.

Encrypted Cuckoo Table An encrypted cuckoo table [23, 28] consists of three arrays \((T_1, T_2, S)\) that hold ciphertexts of some fixed length. The arrays \(T_1\) and \(T_2\) are both of size m and serve as cuckoo hash tables, while S is an array of size s and serves as an auxiliary stash. The data structure uses two hash functions \(h_1,h_2:~ [\ell ] \rightarrow [m]\). Initially, all entries of the arrays are populated with independent encryptions of a special symbol \(\bot \). To retrieve a ciphertext associated with an address i, one decrypts all of the ciphertexts in S, as well as the ciphertexts at \(T_1[h_1[i]]\) and \(T_2[h_2[i]]\) (thus at most \(s+2\) decryptions are performed). If any of these ciphertexts decrypts to a value of the form (iv), then v is the returned output. To insert an address–value pair (iv), encrypt it and write the ciphertext \({\textsf {ct}}\) to position \(T_1[h_1(i)]\), retrieving whatever ciphertext \({\textsf {ct}}_1\) was there before. If the original ciphertext \({\textsf {ct}}_1\) decrypts to \(\bot \), then stop. Otherwise, if \({\textsf {ct}}_1\) decrypts to a pair (jw), then re-encrypt the pair and write the resulting ciphertext to \(T_2[h_2(j)]\), again retrieving whatever ciphertext \({\textsf {ct}}_2\) was there before. If \({\textsf {ct}}_2\) decrypts to \(\bot \), then stop and otherwise continue this process iteratively with ciphertexts \({\textsf {ct}}_3,{\textsf {ct}}_4,\ldots \). If this process continues for \(t = c\log n\) steps, then “give up” and just put the last evicted ciphertext \({\textsf {ct}}_t\) into the first available spot in the stash S. If S is full, then the data structure fails.

We will use the following result sketched in [18]: If \(m=(1+\varepsilon )n\) for some constant \(\varepsilon >0\), and \(h_1,h_2\) are random functions, then after n items are inserted, the probability that S has k or more items written into it is \(O(1/n^{k+2})\). Thus, if S has at least \(\lambda \) slots, then the probability of a failure after n insertions is negligible in \(\lambda \).

Oblivious Table Rebuilds We will assume an oblivious protocol for the following task. At the start of the protocol, the server holds encrypted cuckoo hash tables \(C_1,\ldots ,C_r\). The client has two hash functions \(h_1,h_2\). After the oblivious interaction, the server holds a new cuckoo hash table \(C'_r\) that results from decrypting the data in \(C_1,\ldots ,C_r\), deleting data for duplicated locations with preference given to the copy of the data in the lowest index table, encrypting each index–value pair again and then inserting the ciphertexts into \(C'_r\) using \(h_1,h_2\).

Implementing this task efficiently and obliviously is an intricate task. See [18] and [29] for different methods, which adapt the usage of oblivious sorting first introduced in [16].

ORAM Scheme

We can now describe the scheme of Goodrich and Mitzenmacher [18], with our modifications for next-read pattern hiding. As ingredients, this scheme will use a PRF F and an encryption scheme \(({\textsf {Enc}}, {\textsf {Dec}})\). A visualization of the server’s data structures is given in Fig. 2.

  • \(\mathbf{\textsf {O} }\textsf {Init}(1^\lambda , 1^w, \ell )\):   Let L be the smallest integer such that \(2^{L} > \ell \). The client chooses 2L random keys \(K_{1,1},K_{1,2},\ldots ,K_{L,1},K_{L,2}\) and 2L additional random keys \(R_{1,1},R_{1,2},\ldots ,R_{L,1},R_{L,2}\) to be used for pseudorandom functions and initializes a counter \({\textsf {ctr}}\) to 0. It also selects an encryption key for the IND-CPA secure scheme. It instructs the server to allocate the following data structures:

    • An empty array \(A_0\) that will change size as it is used.

    • L empty cuckoo hash tables \(C_1,\ldots ,C_L\) where the parameters in \(C_j\) are adjusted to hold \(2^{j}\) data items with a negligible (in \(\lambda \)) probability of overflow when used with random hash functions.

    The client state consists of all of the keys \(\left( K_{j,0},K_{j,1}\right) _{j\in [L]}\), \(\left( R_{j,0},R_{j,1}\right) _{j\in [L]}\), the encryption key and \({\textsf {ctr}}\).

  • \(\mathbf{\textsf {O} }\textsf {Read}(i_1,\ldots ,i_t)\):   The client starts by initializing an array \({\textsf {found}}\) of t flags to \({\textsf {false}}\). For each index \(i_j\) to be read, the client does the following. For each level \(k=1,\ldots ,L\), the client executes

    • Let \(C_k = (T^{(k)}_1,T^{(k)}_2,S^{(k)})\)

    • If \({\textsf {found}}[j] = {\textsf {false}}\), read and decrypt all of \(S^{(k)}\), \(T^{(k)}_1[F(K_{k,1},i_j)]\) and \(T^{(k)}_2[F(K_{k,2},i_j)]\). If the data are in any of these slots, set \({\textsf {found}}[j]\) to \({\textsf {true}}\) and remember the value as \(v_j\).

    • Else, if \({\textsf {found}}[j]={\textsf {true}}\), then instead read all of \(S^{(k)}\), \(T^{(k)}_1[F(R_{k,1},i_j\Vert {\textsf {ctr}})]\) and \(T^{(k)}_2[F(R_{k,2},i_j\Vert {\textsf {ctr}})]\) and ignore the results. Note that the counter value is used to create random reads when the state is not reset, while providing the same random values if the state is reset.para Finally, it encrypts and appends \((i_j,v_j)\) to the end of \(A_0\) and continues to the next index \(i_{j+1}\). We note that above, when accessing a table using the output of F, we are interpreting the bit string output by F as a random index from the appropriate range.para After all the indices have been read and written to \(A_0\), the client initiates a rebuild phase, the description of which we defer for now.

  • \(\mathbf{\textsf {O} }\textsf {Write}(i_1,\ldots ,i_t;v_1,\ldots ,v_t)\):   The client encrypts and writes \((i_j,v_j)\) into \(A_0\) for each j and then initiates a rebuild phase described below.

Rebuild Phase We complete the scheme description by describing a rebuild phase, which works as follows.

The client repeats the following process until \(A_0\) is empty:

  • Increment \({\textsf {ctr}}\).

  • Remove and decrypt an item from \(A_0\), calling the result (jv).

  • Let \(r\ge 0\) be the largest integer such that \(2^r\) divides \(({\textsf {ctr}}\, \mod 2^L)\).

  • Select new keys \(K_{r,1},K_{r,2}\) and use the functions \(F(K_{r,1}, \cdot )\) and \(F(K_{r,2},\cdot )\) as \(h_1\) and \(h_2\) to obliviously build a new cuckoo table \(C'_r\) holding the removed item (jv) and all of the data items in \(C_1,\ldots ,C_{r-1}\), freshly re-encrypted and with duplicates removed.

  • Then, for \(j=1\) to \(r-1\), set \(K_{j,1},K_{j,2}\) to fresh random keys and set the cuckoo tables \(C_1,\ldots ,C_r\) to be new, empty tables and \(C_{r}\) to be \(C'_{r}\).

Note that the remaining tables \(C_{r+1},\ldots ,C_L\) are not touched.

We can implement the rebuild phase using the any of the protocols (with small variations) from [18, 19]. The most efficient gives an amortized overhead of \(\log \ell \) operations for all rebuilds, assuming that the client can temporarily locally store \(\ell ^\delta \) memory slots during the protocol (but the client does need to store them between executions of the protocol). If we only allow the client to store a constant number of slots at any one time, then we incur an overhead of \(\log ^2\ell \). In either case, the worst-case overhead is \(O(\ell )\). Using the de-amortization techniques from [19, 27], we can achieve worst-case complexity of \(\log ^2\ell \), at the cost of doubling the server storage. This technique was analyzed in the original ORAM security setting, but it is not hard to extend our proof to show that it preserves next-read pattern hiding as well. At a high level, the technique maintains two copies of the server data (one “old” and one “current”) and performs the table rebuilds on the “old” copy in small chunks of operations after each read or write. But these chunks of operations are determined by a fixed schedule (involving oblivious sorting and simple reorganizations), so during the next-read pattern hiding security game these chunks will be identical after each rewind, and thus, they do not help the adversary.

Next-Read Pattern Hiding

Theorem 2

Assuming that F is a secure PRF, and the underlying encryption scheme is chosen-plaintext secure, then the scheme \(\mathbf{\textsf {O} }\) described above is next-read pattern hiding.


We show that for any efficient adversary \({\mathcal {A}}\), the probabilities that \({\mathcal {A}}\) outputs 1 when playing either \({\mathtt{NextReadGame }}_{\mathcal {A}}^0\) or \({\mathtt{NextReadGame }}_{\mathcal {A}}^1\) differ by only a negligible amount. In these games, the adversary \({\mathcal {A}}\) provides two tuples of operations \(P_1 = (op_1,\ldots ,op_{q_1})\) and \(P_2 = (rop_1,\ldots ,rop_{q_2})\), the latter being all multi-reads, and a permutation \(\pi \) on \([\ell ]\). Then in \({\mathtt{NextReadGame }}_{\mathcal {A}}^0\), \({\mathcal {A}}\) is given the transcript of an honest client and server executing \(P_1\), as well as the transcript of executing the multi-reads in \(P_2\) with rewinds after each operation, while in \({\mathtt{NextReadGame }}_{\mathcal {A}}^1\) it is given the same transcript except that second part is generated by first permuting the addresses in \(P_2\) according to \(\pi \).

We need to argue that these inputs are computationally indistinguishable. For our analysis below, we assume that a rebuild phase never fails, as this event happens with negligible probability in \(\lambda \), as discussed before. We start by modifying the execution of the games in two ways that are shown to be undetectable by \({\mathcal {A}}\). The first change will show that all of the accesses into tables appear to the adversary to be generated by random functions, and the second change will show that the ciphertexts do not reveal any usable information for the adversary.

First, whenever keys \(K_{j,1},K_{j,2}\) are chosen and used with the pseudorandom function F, we use random functions \(g_{j,1},g_{j,2}\) in place of \(F(K_{j,1}, \cdot )\) and \(F(K_{j,2}, \cdot )\).Footnote 11 We do the same for the \(R_{j,1},R_{j,2}\) keys, calling the random functions \(r_{j,1}\) and \(r_{j,2}\). This change only changes the behavior of \({\mathcal {A}}\) by a negligible amount, as otherwise we could build a distinguisher to contradict the PRF security of F via a standard hybrid argument over all of the keys chosen during the game.

The second change we make is that all of the ciphertexts in the transcript are replaced with independent encryptions of equal-length strings of zeros. We claim that this only affects the output distribution of \({\mathcal {A}}\) by a negligible amount, as otherwise we could build an adversary to contradict the IND-CPA security of the underlying encryption scheme via a standard reduction. Here it is crucial that, after each rewind, the client chooses new randomness for the encryption scheme.

We now complete the proof by showing that the distribution of the transcripts given to \({\mathcal {A}}\) is identical in the modified versions of \({\mathtt{NextReadGame }}_{\mathcal {A}}^0\) and \({\mathtt{NextReadGame }}_{\mathcal {A}}^1\). To see why this is true, let us examine what is in one of the game transcripts given to \({\mathcal {A}}\). The transcript for the execution of \(P_1\) consists of \(\mathbf{\textsf {O} }\textsf {Read}\) and \(\mathbf{\textsf {O} }\textsf {Write}\) transcripts, which are accesses to indices in the cuckoo hash tables, ciphertext writes into \(A_0\) and rebuild phases. Finally the execution of \(P_2\) (either permuted by \(\pi \) or not) with rewinds generates a transcript that consists of several accesses to the cuckoo hash tables, each followed by writes to \(A_0\) and a rebuild phase.

By construction of the protocol, in the modified game the only part of the transcript that depends on the addresses in \(P_2\) is the reads into \(T_1^{(k)}\) and \(T_2^{(k)}\) for each k. All other parts of the transcript are oblivious scans of the \(S^{(k)}\) arrays and oblivious table rebuilds which do not depend on the addresses (recall the ciphertexts in these transcripts are encryptions of zeros). Thus we focus on the indices read in each \(T_1^{(k)}\) and \(T_2^{(k)}\) and need to show that, in the modified games, the distribution of these indices does not depend on the addresses in \(P_2\).

The key observation is that, after the execution of \(P_1\), the state of the client is such that each address i will induce a uniformly random sequence of indices in the tables that is independent of the indices read for any other address and independent of the transcript for \(P_1\). If the data are in the cuckoo table at level k, then the indices will be

$$\begin{aligned} \left( g_{j,1}(i)\right) _{j=1}^k \text { and } \left( r_{j,1}(i\Vert {\textsf {ctr}})\right) _{j=k+1}^L. \end{aligned}$$

Thus each i induces a random sequence, and each address will generate an independent sequence. We claim moreover that the sequence for i is independent of the transcript for \(P_1\). This follows from the construction: For the indices derived from \(r_{j,1}\) and \(r_{j,2}\), the transcript for \(P_1\) would have always used a lower value for \({\textsf {ctr}}\). For the indices derived from \(g_{j,1}\) and \(g_{j,2}\), we have that the execution of \(P_1\) would not have evaluated those functions on input i: If i was read during \(P_1\), then i would have been written to \(A_0\) and a rebuild phase would have chosen new random functions for \(g_{j,1}\) and \(g_{j,2}\) before the address–value pair i was placed in the j-th level table again.

With this observation, we can complete the proof. When the modified games are generating the transcript for the multi-read operations in \(P_2\), each individual read for an index i induces an random sequence of table reads among its other oblivious operations. But since each i induces a completely random sequence and permuting the addresses will only permute the random sequences associated with the addresses, the distribution of the transcript is unchanged. Thus no adversary can distinguish these games, which means that no adversary could distinguish \({\mathtt{NextReadGame }}_{\mathcal {A}}^0\) and \({\mathtt{NextReadGame }}_{\mathcal {A}}^1\), as required.\(\square \)

Authenticity, Extensions and Optimizations

Authenticity To achieve authenticity, we sketch how to employ the technique introduced in [16]. A straightforward attempt is to tag every ciphertext stored on the server along with its location on the server using a message authentication code (MAC). But this fails because the sever can “roll back” changes to the data by replacing ciphertexts with previously stored ones at the same location. We can generically fix this by using the techniques of memory checking [5, 13, 26] at some additional logarithmic overhead. However, it also turns out that authenticity can also be added at almost no cost to several specific constructions, as we describe below.

Goldreich and Ostrovsky showed that any ORAM protocol supporting time-labeled simulation (TLS) can be modified to achieve authenticity without much additional complexity. We say that an ORAM protocol supports TLS if there exists an efficient algorithm Q such that, after the j-th message is sent to the server, for each index x on the server memory, the number of times x has been written to is equal to Q(jx).Footnote 12 Overall, one implements the above tagging strategy and also includes Q(jx) with the data being tagged, and when reading one recomputes Q(jx) to verify the tag.

Our scheme can be shown to support TLS in a manner very similar to the original hierarchical scheme [16]. The essential observation, also used there, is that the table indices are only written to during a rebuild phase, so by tracking the number of executed rebuild phases we can compute how many times each index of the table was written to.

Extensions and Optimizations The scheme above is presented in a simplified form that can be made more efficient in several ways while maintaining security.

  • The keys in the client state can be derived from a single key by appropriately using the PRF. This shrinks the client state to a single key and counter.

  • The initial table \(C_1\) can be made larger to reduce the number of rebuild phases (although this does not affect the asymptotic complexity).

  • We can collapse the individual oblivious table rebuilds into one larger rebuild.

  • It was shown in [20] that all of the L cuckoo hash tables can share a single \(O(\lambda )\)-size stash S while still maintaining a negligible chance of table failure.

  • Instead of doing table rebuilds all at once, we can employ a technique that allows for them to be done incrementally, allowing us to achieve worst-case rather than amortized complexity guarantees [19, 27]. These techniques come at the cost of doubling the server storage.

  • The accesses to cuckoo tables on each level during a multi-read can be done in parallel, which reduces the round complexity of that part to be independent of t, the number of addresses being read.

  • The ORAM can be initialized with data by the client by locally simulating the protocol writes and then uploading the initial server state. The proof of security for this version is via a trivial reduction, where the server does not see the initial batch of operations.

Alternatively, one can modify the ORAM setup to initially store all data on the last table (obliviously, of course, using a PRF) to avoid the local simulation (see e.g., [19]). The proof of security for this approach is also a trivial reduction, where the reduction repeats operations until a rebuild phase is triggered that moves all of the data to the last level.

We can also extend this scheme to support a dynamically changing memory size. This is done by simply allocating different sized tables during a rebuild that eliminates the lower larger tables or adds new ones of the appropriate size. This modification will achieve next-read pattern hiding security, but it will not be standard pattern hiding secure, as it leaks some information about the number of memory slots in use. One can formalize this, however, in a pattern hiding model where any two sequences with equal memory usage are required to be indistinguishable.

Adaptive Security We observe that it is easy prove this ORAM meets the adaptive variant of next-read pattern hiding from the end of Sect. 4. Recall that in this version \(P_1\) can be chosen adaptively, and then \(P_2\) is chosen all at once afterward. The argument above still justifies substituting in the random functions \(g_{j,1},g_{j,2},r_{j,1},r_{j,2}\) and also the substitution of real ciphertexts with encryptions of zeros. In this version of the adaptive game, all of the accesses into the tables in the observed transcript will be for uniform and independent indices. Moreover, in the adversary’s view, the accessed indices are independent of the addresses it adaptively selects (formally, we can choose the random indices before the adversary selects the addresses and then “program” them into the random functions without changing the distribution of the experiment). This continues to hold during the execution of \(P_2\), which is anyway fixed all at once.

Efficiency of the ORAM Instantiation In this scheme, the client stores the counter and the keys, which can be derived from a single key using the PRF. The server stores \(\log \ell \) tables, where the j-th table requires \(2^j + \lambda \) memory slots, which sums to \(O(\ell + \lambda \cdot \log \ell )\). Using the optimization above, we only need a single stash, reducing the sum to \(O(\ell + \lambda )\). When executing \(\mathbf{\textsf {O} }\textsf {Read}\), each index read requires accessing two slots plus the \(\lambda \) stash slots in each of the \(\log \ell \) tables, followed by a rebuild. \(\mathbf{\textsf {O} }\textsf {Write}\) is simply one write followed by a rebuild phase. Table 1 summarizes the efficiency measures of thescheme.

Table 1 Efficiency of ORAM scheme above


We now look at the efficiency of our PORAM construction, when instantiated with the ORAM scheme from Sect. 6 (we assume the rebuild phases are implemented via the Goodrich–Mitzenmacher algorithm [18] with the worst-case complexity optimization [19, 27].) Since our PORAM scheme preserves (standard) ORAM security, we analyze its efficiency in two ways. Firstly, we look at the overhead of \(\textsf {{PORAM}}\) scheme on top of just storing the data inside of the ORAM without attempting to achieve any PoR security (e.g., not using any error-correcting code). Secondly, we look at the overall efficiency of PORAM. Third, we compare it with dynamic PDP [14, 35], which does not employ erasure codes and does not provide full retrievability guarantee, and hence show the overhead of providing full retrievability guarantee using a single audit with our scheme. In the table below, \(\ell \) denotes the size of the client data and \(\lambda \) is the security parameter. We assume that the ORAM scheme uses a PRF whose computation takes \(O(\lambda )\) work.Footnote 13

PORAM efficiency vs. ORAM Overall vs. Dynamic PDP [14, 37]
Client storage Same \(O(\lambda )\) Same
Server storage \(\times ~O(1)\) \(O(\ell )\) \(\times ~O(1)\)
Read complexity \(\times ~O(1)\) \(O(\lambda \log ^2 \ell )\) \(\times ~ O(\log \ell )\)
Write complexity \(\times ~O(\lambda )\) \(O(\lambda ^2 \log ^2 \ell )\) \(\times ~ O(\lambda \log \ell )\)
Audit complexity Read \(\times ~O(\lambda )\) \(O(\lambda ^2 \log ^2 \ell )\) \(\times ~ O(\log \ell )\)

Like previous work building and using ORAM, these asymptotic measures hide what may be large constants. The server storage is roughly four times the plaintext size (not counting stashes, which grow slowly compared to the plaintext size). Read operations are exactly ORAM reads, and write operations require n invocations of each of the read and write ORAM protocols, which themselves invoke an oblivious sorting subroutine several times.

We can get a rough idea of the overhead using the experimental reports for this type of ORAM due to Pinkas and Reinman [29] (this is the only implementation we are aware of. Note that their construction was later shown to be insecure and required the addition of stashes, which is why it is only a rough estimate). For a memory with \(2^{20}\) slots, their implementation showed the hidden constant in the \(O(\lambda \cdot \log ^2 \ell )\) to be 165. With our suggested setting of \(n=256\), we estimate that writes in our protocol will have an overhead of about \(256\cdot 165\cdot \lambda ^2 \log ^2\ell \). Given the large constants here, it is likely worth exploring more recent ORAM constructions like Path ORAM [34] with good implementations.

By modifying the underlying ORAM to dynamically resize tables during rebuilds, the resulting PORAM instantiation will achieve the same efficiency measures as above, but with \(\ell \) taken to be amount of memory currently used by the memory access sequence. This is in contrast to the usual ORAM setting where \(\ell \) is taken to be a (perhaps large) upper bound on the total amount of memory that will ever be used.

Lastly, we note that after our work [8], two recent works emerged [9, 32]. Both of them achieve better efficiency compared to our work, mainly cutting of the squares in our complexity measures. Shi et al. [32] even provide an efficient implementation of their scheme. They rely on similar observations to ours in the sense that the write and audit operations need to be implemented obliviously (using similar techniques to those of the hierarchical ORAM schemes), though they show that the read operations may be performed on a separate data structure in a non-oblivious manner. This is indeed the main difference between a full ORAM scheme and a DPOR scheme. DPOR security definition does not require hiding the full access pattern, whereas ORAM security definition does. Since our PORAM is both an ORAM and a DPOR scheme, its advantage is that by having some logarithmic overhead over [9, 32], it also hides the access pattern of the client.