Abstract
Oblivious RAM (ORAM) is a powerful cryptographic building block that allows a program to provably hide its access patterns to sensitive data. Since the original proposal of ORAM by Goldreich and Ostrovsky, numerous improvements have been made. To date, the best asymptotic overhead achievable for general block sizes is \(O(\log ^2 N/\log \log N)\), due to an elegant scheme by Kushilevitz et al., which in turn relies on the oblivious Cuckoo hashing scheme by Goodrich and Mitzenmacher.
In this paper, we make the following contributions: we first revisit the prior \(O(\log ^2 N/\log \log N)\)overhead ORAM result. We demonstrate the somewhat incompleteness of this prior result, due to the subtle incompleteness of a core building block, namely, Goodrich and Mitzenmacher’s oblivious Cuckoo hashing scheme.
Even though we do show how to patch the prior result such that we can fully realize Goodrich and Mitzenmacher’s elegant blueprint for oblivious Cuckoo hashing, it is clear that the extreme complexity of oblivious Cuckoo hashing has made understanding, implementation, and proofs difficult. We show that there is a conceptually simple \(O(\log ^2 N/\log \log N)\)overhead ORAM that dispenses with oblivious Cuckoo hashing entirely.
We show that such a conceptually simple scheme lends to further extensions. Specifically, we obtain the first \(O(\log ^2 N/\log \log N)\) Oblivious Parallel RAM (OPRAM) scheme, thus not only matching the performance of the best known sequential ORAM, but also achieving superlogarithmic improvements in comparison with known OPRAM schemes.
The full version of this paper is available on Cryptology ePrint Archive [7].
You have full access to this open access chapter, Download conference paper PDF
Similar content being viewed by others
Keywords
1 Introduction
Oblivious RAM [19, 20, 37], originally proposed in the seminal work by Goldreich and Ostrovsky [19, 20], is a powerful cryptographic primitive that provably obfuscates a program’s access patterns to sensitive data. Since Goldreich and Ostrovsky’s original work [19, 20], numerous subsequent works have proposed improved constructions, and demonstrated a variety of ORAM applications in both theoretical contexts (e.g., multiparty computation [23, 27], Garbled RAMs [18, 28]) as well as in secure hardware and software systems (e.g., secure processors [15, 16, 29, 36], and cloud outsourcing [22, 35, 38, 39, 43]). To hide access patterns, an ORAM scheme typically involves reading, writing, or shuffling multiple blocks for every data request. Suppose that on average, for each data request, an ORAM scheme must read/write X blocks. In this paper, we refer to X as the overhead (or the total work blowup) of the ORAM scheme.
Goldreich and Ostrovsky [19, 20] showed that, roughly speaking, any “natural” ORAM scheme that treats each block as an “opaque ball” must necessarily suffer from at least logarithmic overhead. The recent Circuit ORAM [41] work demonstrated an almost matching upper bound for large enough blocks. Let N denote the total memory size. Circuit ORAM showed the existence of a statistically secure ORAM scheme that achieves \(O(\alpha \log N)\) overhead for \(N^\epsilon \)bit blocks for any constant \(\epsilon > 0\) and any superconstant function \(\alpha = \omega (1)\). To date, the existence of an almost logarithmic ORAM scheme is only known for large blocks. For general block sizes, the state of affairs is different: the best known construction (asymptotically speaking) is a computationally secure scheme by Kushilevitz et al. [26], which achieves \(O(\frac{\log ^2 N}{\log \log N})\) overhead assuming block sizes of \(\varOmega (\log N)\) ^{Footnote 1}. We note that all known ORAM schemes assume that a memory block is at least large enough to store its own address, i.e., at least \(\varOmega (\log N)\) bits long. Therefore, henceforth in this paper, we use the term “general block size” to refer to a block size of \(\varOmega (\log N)\).
Although most practical ORAM implementations (in the contexts of secure multiparty computation, secure processors, and storage outsourcing) opted for treebased ORAM constructions [37, 40, 41] due to tighter practical constants, we note that hierarchical ORAMs are nonetheless of much theoretical interest: for example, when the CPU has \(O(\sqrt{N})\) private cache, hierarchical ORAMs can achieve \(O(\log N)\) simulation overhead while a comparable result is not known in the treebased framework. Recent works [3, 8] have also shown how hierarchical ORAMs can achieve asymptotically better locality and IO performance than known treebased approaches.
Our contributions. In this paper, we make the following contributions:

Revisit \(O(\log ^2 N/\log \log N)\) ORAMs. We revisit how to construct a computationally secure ORAM with \(O(\frac{\log ^2 N}{\log \log N})\) overhead for general block sizes. First, we show why earlier results along this front [22, 26] are somewhat incomplete due to the incompleteness of a core building block, oblivious Cuckoo hashing, that is proposed and described by Goodrich and Mitzenmacher [22]. Next, besides fixing and restating the earlier results regarding the existence of an \(O(\log ^2 N/\log \log N)\) ORAM, perhaps more compellingly, we show how to obtain an ORAM with the same asymptotic overhead, but in a conceptually much simpler manner, completely obviating the need to perform oblivious Cuckoo hashing [22] which is the center of complexity in the earlier result [26].

New results on efficient OPRAMs. Building on our new ORAM scheme, we next present the first Oblivious Parallel RAM (OPRAM) construction that achieves \(O(\frac{\log ^2 N}{\log \log N})\) simulation overhead. To the best of our knowledge, our OPRAM scheme is the first one to asymptotically match the best known sequential ORAM scheme for general block sizes. Moreover, we achieve a superlogarithmic factor improvement over earlier works [5, 10] and over the concurrent work by Nayak and Katz [31] (see further clarifications in Sect. 1.3).
We stress that our conceptual simplicity and modular approach can open the door for possible improvements. For example, our OPRAM results clearly demonstrate the benefits of having a conceptually clean hierarchical ORAM framework: had we tried to make (a corrected variant of) Kushilevitz et al. [26] into an OPRAM, it is not clear whether we could have obtained the same performance. In particular, achieving \(O(\log ^2 N/ \log \log N)\) worstcase simulation overhead requires deamortizing a parallel version of their oblivious cuckoo hash rebuilding algorithm, and moreover, work and depth have to be deamortized at the same time—and we are not aware of a way to do this especially due to the complexity of their algorithm.
1.1 Background on Oblivious Hashing and Hierarchical ORAMs
In this paper, we consider the hierarchical framework, originally proposed by Goldreich and Ostrovsky [19, 20], for constructing ORAM schemes. At a high level, this framework constructs an ORAM scheme by having exponentially growing levels of capacity \(1, 2, 4, \ldots , N\) respectively, where each smaller level can be regarded as a “stash” for larger levels. Each level in the hierarchy is realized through a core abstraction henceforth called oblivious hashing in the remainder of this paper. Since oblivious hashing is the core abstraction we care about, we begin by explicitly formulating oblivious hashing as the following problem:

Functional abstraction. Given an array containing n possibly dummy elements where each nondummy element is a (key, value) pair, design an efficient algorithm that builds a hash table data structure, such that after the building phase, each element can be looked up by its key consuming a small amount of time and work. In this paper, we will assume that all nondummy elements in the input array have distinct keys.

Obliviousness. The memory access patterns of both the building and lookup phases do not leak any information (to a computationally bounded adversary) about the initial array or the sequence of lookup queries Q—as long as all nondummy queries in Q are distinct. In particular, obliviousness must hold even when Q may contain queries for elements not contained in the array in which case the query should return the result \(\bot \). The correct answer to a dummy query is also \(\bot \) by convention.
Not surprisingly, the performance of a hierarchical ORAM crucially depends on the core building block, oblivious hashing. Here is the extent of our knowledge about oblivious hashing so far:

Goldreich and Ostrovsky [19, 20] show an oblivious variant of normal ballsandbins hashing that randomly throws n elements into n bins. They show that obliviously building a hash table containing n elements costs \(O(\alpha n \log n \log \lambda )\) work, and each query costs \(O(\alpha \log \lambda )\) work. If \(\alpha \) is any superconstant function, we can attain a failure probability \(\mathsf{negl}(\lambda )\). This leads to an \(O(\alpha \log ^3 N)\)overhead ORAM scheme, where N is the total memory size^{Footnote 2}.

Subsequently, Goodrich and Mitzenmacher [22] show that the Cuckoo hashing algorithm can be made oblivious, incurring \(O(n \log n)\) total work for building a hash table containing n elements, and only O(1) query cost (later we will argue why their oblivious hashing scheme is somewhat incomplete). This leads to an ORAM scheme with \(O(\log ^2 N)\)overhead.

Kushilevitz et al. [26] in turn showed an elegant reparametrization trick atop the Goodrich and Mitzenmacher ORAM, thus improving the overhead to \(O(\frac{\log ^2 N}{\log \log N})\). Since Kushilevitz et al. [26] crucially rely on Goodrich and Mitzenmacher’s oblivious Cuckoo hashing scheme, incompleteness of the hashing result in some sense carries over to their \(O(\frac{\log ^2 N}{\log \log N})\) overhead ORAM construction.
1.2 Technical Roadmap
Revisit oblivious Cuckoo hashing. Goodrich and Mitzenmacher [22]’s blueprint for obliviously building a Cuckoo hash table is insightful and elegant. They express the task of Cuckoo hash table rebuilding as a MapReduce task (with certain nice properties), and they show that any such MapReduce algorithm has an efficient oblivious instantiation.
Fundamentally, their construction boils down using a sequence of oblivious sorts over arrays of (roughly) exponentially decreasing lengths. To achieve full privacy, it is necessary to hide the true lengths of these arrays during the course of the algorithm. Here, Goodrich and Mitzenmacher’s scheme description and their proof appear inconsistent: their scheme seems to suggest padding each array to the maximum possible length for security—however, this would make their scheme \(O(\log ^3 N)\) overhead rather than the claimed \(O(\log ^2 N)\). On the other hand, their proof appears only to be applicable, if the algorithm reveals the true lengths of the arrays—however, as we argue in detail in the online full version [7], the array lengths in the cuckoo hash rebuilding algorithm contain information about the size of each connected component in the cuckoo graph. Thus leaking array lengths can lead to an explicit attack that succeeds with nonnegligible probability: at a high level, this attack tries to distinguish two request sequences, one repeatedly requesting the same block whereas the other requests disctinct blocks. The latter request sequence will cause the cuckoo graph in the access phase to resemble the cuckoo graph in the rebuild phase, whereas the former request sequence results in a fresh random cuckoo hash graph for the access phase (whose connected component sizes are different than the rebuild phase with relatively high probability).
As metioned earlier, the incompleteness of oblivious Cuckoo hashing also makes the existence proof of an \(O(\log ^2 N/\log \log N)\)overhead ORAM somewhat incomplete.
Is oblivious Cuckoo hashing necessary for efficient hierarchical ORAM? Goodrich and Mitzenmacher’s oblivious Cuckoo hashing scheme is extremely complicated. Although we do show in our online full version [7] that the incompleteness of Goodrich and Mitzemacher’s construction and proofs can be patched, thus correctly and fully realizing the elegant blueprint they had in mind—the resulting scheme nonetheless suffers from large constant factors, and is unsuitable for practical implementation. Therefore, a natural question is, can we build efficient hierarchical ORAMs without oblivious Cuckoo hashing?
Our first insight is that perhaps oblivious Cuckoo hashing scheme is an overkill for constructing efficient hierarchical ORAMs after all. As initial evidence, we now present an almost trivial modification of the original Goldreich and Ostrovsky oblivious ballsandbins hashing scheme such that we can achieve an \(O(\alpha {\log ^2 N})\)overhead ORAM for any superconstant function \(\alpha \).
Recall that Goldreich and Ostrovsky [19, 20] perform hashing by hashing n elements into n bins, each of \(O(\alpha \log \lambda )\) capacity, where \(\lambda \) is the security parameter. A simple observation is the following: instead of having n bins, we can have \(\frac{n}{\alpha \log \lambda }\) bins—it is not hard to show that each bin’s occupancy will still be upper bounded by \(O(\alpha \log \lambda )\) except with \(\mathsf{negl}(\lambda )\) probability. In this way, we reduce the size of the hash table by a \(\log \lambda \) factor, and thus the hash table can be obliviously rebuilt in logarithmically less time. Plugging in this new hash table into Goldreich and Ostrovsky’s ORAM construction [19, 20], we immediately obtain an ORAM scheme with \(O(\alpha \log ^2 N)\) overhead.
This shows that through a very simple construction we can almost match Goodrich and Mitzenmacher’s ORAM result [22]. This simple scheme does not quite get us to where we aimed to be, but we will next show that oblivious Cuckoo hashing is likewise an overkill for constructing \((\frac{\log ^2 N}{\log \log N})\)overhead ORAMs.
Conceptually simple \((\frac{\log ^2 N}{\log \log N})\) overhead ORAM. Recall that a hierarchical ORAM’s overhead is impacted by two cost metrics of the underlying oblivious hashing scheme, i.e., the cost of building the hashtable, and the cost of each lookup query. Goodrich and Mitzenmacher’s oblivious Cuckoo hashing scheme [22] minimizes the lookup cost to O(1), but this complicates the building of the hashtable.
Our key insight is that in all known hashingbased hierarchical ORAM constructions [19, 20, 22, 26], the resulting ORAM’s cost is dominated by the hashtable rebuilding phase, and thus it may be okay if the underlying hashing scheme is more expensive in lookup. More specifically, to obtain an \(O(\frac{\log ^2 N}{\log \log N})\) ORAM, we would like to apply Kushilevitz et al. [26]’s reparametrized version of the hierarchical ORAM. Kushilevitz et al. [26] showed that their reparametrization technique works when applied over an oblivious Cuckoo hashing scheme. We observe that in fact, Kushilevitz et al. [26]’s reparametrization technique is applicable for a much broader parameter range, and concretely for any oblivious hashing scheme with the following characteristics:

It takes \(O(n \log n)\) total work to build a hash table of n elements—in other words, the perelement building cost is \(O(\log n)\).

The lookup cost is asymptotically smaller than the perelement building cost—specifically, \(O(\log ^\epsilon \lambda )\) lookup cost suffices where \(\epsilon \in (0.5, 1)\) is a suitable constant.
This key observation allows us to relax the lookup time on the underlying oblivious hashing scheme. We thus propose a suitable oblivious hashing scheme that is conceptually simple. More specifically, our starting point is a (variant of a) twotier hashing scheme first described in the elegant work by Adler et al. [1]. In a twotier hashing scheme, there are two hash tables denoted \(\mathsf{H}_1\) and \(\mathsf{H}_2\) respectively, each with \(\frac{n}{\log ^\epsilon \lambda }\) bins of \(O(\log ^\epsilon \lambda )\) capacity, where \(\epsilon \in (0.5, 1)\) is a suitable constant. To hash n elements (nonobliviously), we first throw each element into a random bin in \(\mathsf{H}_1\). For all the elements that overflow its bin capacity, we throw them again into the second hash table \(\mathsf{H}_2\). Stochastic bounds show that the second hash table \(\mathsf{H}_2\) does not overflow except with \(\mathsf{negl}(\lambda )\) probability. Clearly, the lookup cost is \(O(\log ^\epsilon \lambda )\); and we will show that the hash table building algorithm can be made oblivious through O(1) number of oblivious sorts.
New results on oblivious parallel RAM. The conceptual simplicity of our ORAM scheme not only makes it easier to understand and implement, but also lends to further extensions. In particular, we construct a computationally secure OPRAM scheme that has \(O(\log ^2 N/\log \log N)\) overhead—to the best of our knowledge, this is the first OPRAM scheme that matches the best known sequential ORAM in performance for general block sizes. Concretely, the hierarchical lookup phase can be parallelized using the standard conflict resolution (proposed by Boyle et al. [5]) as this phase is readonly. In the rebuild phase, our twotier oblivious hashing takes only O(1) number of oblivious sort and linear scan that marks excess elements, which can be parallelized with known algorithms, i.e. range prefix sum.
As mentioned earlier, our modular approach and conceptual simplicity turned out to be a crucial reason why we could turn our ORAM scheme into an OPRAM—it is not clear whether (a corrected version of) Kushilevitz et al. [26] is amenable to the same kind of transformation achieving the same overhead due to complications in deamortizing their cuckoo hash rebuilding algorithm. Thus we argue that our conceptually simple framework can potentially lend to other possible applications and improvements.
1.3 Related Work
ORAMs. ORAM was first proposed in a seminal work by Goldreich and Ostrovsky [19, 20] who showed a computationally secure scheme with \(O(\alpha \log ^3 N)\) overhead for general block sizes and for any superconstant function \(\alpha = \omega (1)\). Subsequent works improve the hierarchical ORAM [22, 26] and show that \(O(\frac{\log ^2 N}{\log \log N})\) overhead can be attained under computational security—our paper points out several subtleties and the incompleteness of the prior results; additionally, we show that it is possible to obtain such an \(O(\frac{\log ^2 N}{\log \log N})\) overhead in a conceptually much simpler manner.
Besides the hierarchical framework, Shi et al. [37] propose a treebased paradigm for constructing ORAMs. Numerous subsequent works [11, 40, 41] improved treebased constructions. With the exception of a few works [14], the treebased framework was primarily considered for the construction of statistically secure ORAMs. The performance of treebased ORAMs depend on the block size, since with a larger block size we can reduce the number of recursion levels in these constructions. The recent Circuit ORAM work [41] shows that under block sizes as large as \(N^\epsilon \) for any arbitrarily small constant \(\epsilon \), we can achieve \(\alpha \log N\) bandwidth overhead for an arbitrary superconstant function \(\alpha = \omega (1)\)—this also shows the (near) tightness of the GoldreichOstrovsky lower bound [19, 20] showing that any ORAM scheme must necessarily incur logarithmic overhead. Note that under block sizes of at least \(\log ^{1 + \epsilon } N\) for an arbitrarily small constant \(\epsilon \), Circuit ORAM [41] can also attain \(O(\frac{\log ^2 N}{\log \log N})\) overhead and it additionally achieves statistical security rather than computational.
OPRAMs. Since modern computing architectures such as cloud platforms and multicore architectures exhibit a high degree of parallelism, it makes sense to consider the parallel counterpart of ORAM. Oblivious Parallel ORAM (OPRAM) was first proposed by Boyle et al. [5], who showed a construction with \(O(\alpha \log ^4 N)\) overhead for any superconstant function \(\alpha \). Boyle et al.’s result was later improved by Chen et al. [10], who showed how to achieve \(O(\alpha \log ^3 N)\) overhead with polylogarithmic CPU private cache—their result also easily implies an \(O(\alpha \log ^3 N \log \log N)\) overhead OPRAM with O(1) CPU private cache, the setting that we focus on in this paper for generality.
A concurrent and independent manuscript by Nayak et al. [31] further improves the CPUmemory communication by extending Chen et al.’s OPRAM [10]. However, their scheme still requires \(O(\alpha \log ^3 N \log \log N)\) CPUCPU communication which was the dominant part of the overhead in Chen et al. [10]. Therefore, under a general notion of overhead that includes both CPUCPU communication and CPUmemory communication, Nayak et al.’s scheme still has the same asymptotic overhead^{Footnote 3} as Chen et al. [10] which is more than a logarithmic factor more expensive in comparison with our new OPRAM construction.
In a companion paper, Chan et al. [9] showed how to obtain statistically secure and computationally secure OPRAMs in the treebased ORAM framework. Specifically, they showed that for general block sizes, we can achieve statistically secure OPRAM with \(O(\log ^2 N)\) simulation overhead and computationally secure OPRAM with \(O(\log ^2 N/\log \log N)\) simulation overhead. For the computationally secure setting, Chan et al. [9] achieves the same asymptotical overhead as this paper, but the two constructions follow different paradigms so we believe that they are both of value. In another recent work, Chan et al. [6] proposed a new notion of depth for OPRAMs where the OPRAM is allowed to have more CPUs than the original PRAM to further parallelize the computation. In this paper, an OPRAM’s simulation overhead is defined as its runtime blowup assuming that the OPRAM consumes the same number of CPUs as the PRAM.
Nonoblivious techniques for hashing. Many hashing schemes [4, 12, 17, 25, 30] were considered in the (parallel) algorithms literature. Unfortunately, most of them are not good candidates for constructing efficient ORAM and OPRAM schemes since there is no known efficient and oblivious counterpart for the algorithm. We defer detailed discussions of these related works to our online full version [7].
2 Definitions and Building Blocks
2.1 Parallel Random Access Machines
We define a Parallel Random Access Machine (PRAM) and an Oblivious Parallel Random Access Machine (OPRAM) in a similar fashion as Boyle et al. [5] as well as Chan and Shi [9]. Some of the definitions in this section are borrowed verbatim from Boyle et al. [5] or Chan and Shi [9].
Although we give definitions only for the parallel case, we point out that this is without loss of generality, since a sequential RAM can be thought of as a specialcase PRAM.
Parallel Random Access Machine (PRAM). A parallel randomaccess machine (PRAM) consists of a set of CPUs and a shared memory denoted \({\mathsf {mem}} \) indexed by the address space \([N] := \{1,2, \ldots , N\}\). In this paper, we refer to each memory word also as a block, and we use D to denote the bitlength of each block.
We support a more general PRAM model where the number of CPUs in each time step may vary. Specifically, in each step \(t \in [T]\), we use \(m_t\) to denote the number of \(\text {CPUs}\). In each step, each CPU executes a next instruction circuit denoted \(\varPi \), updates its CPU state; and further, CPUs interact with memory through request instructions \(\mathbf {I}^{(t)} := (I^{(t)}_i: i \in [m_t])\). Specifically, at time step t, CPU i’s instruction is of the form \(I^{(t)}_i := (\mathtt{read}, {\mathsf {addr}})\), or \(I^{(t)}_i := (\mathtt{write}, {\mathsf {addr}}, {\mathsf {data}})\) where the operation is performed on the memory block with address \({\mathsf {addr}} \) and the block content \({\mathsf {data}} \in \{0,1\}^D \cup \{\bot \}\).
If \(I^{(t)}_i = (\mathtt{read}, {\mathsf {addr}})\) then the \(\text {CPU} \) i should receive the contents of \({\mathsf {mem}} [{\mathsf {addr}} ]\) at the beginning of time step t. Else if \(I^{(t)}_i = (\mathtt{write}, {\mathsf {addr}}, {\mathsf {data}})\), \(\text {CPU} \) i should still receive the contents of \({\mathsf {mem}} [{\mathsf {addr}} ]\) at the beginning of time step t; further, at the end of step t, the contents of \({\mathsf {mem}} [{\mathsf {addr}} ]\) should be updated to \({\mathsf {data}} \).
Write conflict resolution. By definition, multiple \(\mathtt{read}\) operations can be executed concurrently with other operations even if they visit the same address. However, if multiple concurrent \(\mathtt{write}\) operations visit the same address, a conflict resolution rule will be necessary for our PRAM be welldefined. In this paper, we assume the following:

The original PRAM supports concurrent reads and concurrent writes (CRCW) with an arbitary, parametrizable rule for write conflict resolution. In other words, there exists some priority rule to determine which \(\mathtt{write}\) operation takes effect if there are multiple concurrent writes in some time step t.

Our compiled, oblivious PRAM (defined below) is a “concurrent read, exclusive write” PRAM (CREW). In other words, our OPRAM algorithm must ensure that there are no concurrent writes at any time.
We note that a CRCWPRAM with a parametrizable conflict resolution rule is among the most powerful CRCWPRAM model, whereas CREW is a much weaker model. Our results are stronger if we allow the underlying PRAM to be more powerful but the our compiled OPRAM uses a weaker PRAM model. For a detailed explanation on how stronger PRAM models can emulate weaker ones, we refer the reader to the work by Hagerup [24].
CPUtoCPU communication. In the remainder of the paper, we sometimes describe our algorithms using CPUtoCPU communication. For our OPRAM algorithm to be oblivious, the interCPU communication pattern must be oblivious too. We stress that such interCPU communication can be emulated using shared memory reads and writes. Therefore, when we express our performance metrics, we assume that all interCPU communication is implemented with shared memory reads and writes. In this sense, our performance metrics already account for any interCPU communication, and there is no need to have separate metrics that characterize interCPU communication. In contrast, some earlier works [10] adopt separate metrics for interCPU communication.
Additional assumptions and notations. Henceforth, we assume that each CPU can only store O(1) memory blocks. Further, we assume for simplicity that the runtime of the PRAM, the number of CPUs activited in each time step and which CPUs are activited in each time step are fixed a priori and publicly known parameters. Therefore, we can consider a PRAM to be a tuple
where \(\varPi \) denotes the next instruction circuit, N denotes the total memory size (in terms of number of blocks), T denotes the PRAM’s total runtime, and \(P_t\) denotes the set of CPUs to be activated in each time step \(t \in [T]\), where \(m_t := P_t\).
Finally, in this paper, we consider PRAMs that are stateful and can evaluate a sequence of inputs, carrying state across in between. Without loss of generality, we assume each input can be stored in a single memory block.
2.2 Oblivious Parallel RandomAccess Machines
Randomized PRAM. A randomized PRAM is a special PRAM where the CPUs are allowed to generate private random numbers. For simplicity, we assume that a randomized PRAM has a priori known, deterministic runtime, and that the CPU activation pattern in each time step is also fixed a priori and publicly known.
Memory access patterns. Given a PRAM program denoted \(\mathsf{PRAM}\) and a sequence of inputs \(({{\mathsf{inp}}}_1, \ldots , {{\mathsf{inp}}}_d)\), we define the notation \(\mathsf{Addresses}[\mathsf{PRAM}]({{\mathsf{inp}}}_1, \ldots , {{\mathsf{inp}}}_d)\) as follows:

Let T be the total number of parallel steps that \(\mathsf{PRAM}\) takes to evaluate inputs \(({{\mathsf{inp}}}_1, \ldots , {{\mathsf{inp}}}_d)\).

Let \(A_t := \left\{ (\textsf {cpu}^t_1, {\mathsf {addr}} ^t_1), (\textsf {cpu}^t_2, {\mathsf {addr}} ^t_2) \ldots , (\textsf {cpu}^t_{m_t}, {\mathsf {addr}} ^t_{m_t}) \right\} \) be the list of (CPU id, address) pairs such that \(\textsf {cpu}^t_i\) accessed memory address \({\mathsf {addr}} ^t_i\) in time step t.

We define \(\mathsf{Addresses}[\mathsf{PRAM}]({{\mathsf{inp}}}_1, \ldots , {{\mathsf{inp}}}_d)\) to be the random variable \(\left[ A_t \right] _{t \in [T]}\).
Oblivious PRAM (OPRAM). A randomized \(\mathsf{PRAM}\) is said to be computationally oblivious, iff there exists a \({\text {p.p.t.}}\) simulator \({\mathsf {Sim}} \), and a negligible function \(\epsilon (\cdot )\) such that for any input sequence \(({{\mathsf{inp}}}_1, \ldots , {{\mathsf{inp}}}_d)\) where \({{\mathsf{inp}}}_i \in \{0, 1\}^D\) for \(i \in [d]\),
where \(\overset{\epsilon (N)}{\approx }\) means that no \({\text {p.p.t.}}\) adversary can distinguish the two probability ensembles except with \(\epsilon (N)\) probability.
In other words, obliviousness requires that there is a polynomialtime simulator \({\mathsf {Sim}} \) that can simulate the memory access patterns knowing only the memory size N, the number of inputs d, the parallel runtime T for evaluating the inputs, as well as the apriori fixed CPU activation pattern \((P_t: t \in [T])\). In particular, the simulator \({\mathsf {Sim}} \) does not know anything about the sequence of inputs.
Oblivious simulation and simulation overhead. We say that a oblivious PRAM, denoted as \(\mathsf{OPRAM}\), simulates a \(\mathsf{PRAM}\) if for every input sequence \(({{\mathsf{inp}}}_1, \ldots , {{\mathsf{inp}}}_d)\), \(\mathsf{OPRAM}({{\mathsf{inp}}}_1, \ldots , {{\mathsf{inp}}}_d) = \mathsf{PRAM}({{\mathsf{inp}}}_1, \ldots , {{\mathsf{inp}}}_d)\), i.e., \(\mathsf{OPRAM}\) and \(\mathsf{PRAM}\) output the same outcomes on any input sequence. In addition, an OPRAM scheme is a randomized PRAM algorithm such that, given any PRAM, the scheme compiles PRAM into an oblivious PRAM, OPRAM, that simulates PRAM.
For convenience, we often adopt two intermediate metrics in our descriptions, namely, total work blowup and parallel runtime blowup. We say that an OPRAM scheme has a total work blowup of x and a parallel runtime blowup of y, iff for every PRAM step t in which the PRAM consumes \(m_t\) CPUs, the OPRAM can complete this step with \(x \cdot m_t\) total work and in y parallel steps—if the OPRAM is allowed to consume any number of CPUs (possibly greater than \(m_t\)).
Fact 1
If there exists an OPRAM with x total work blowup and y parallel runtime blowup such that \(x \ge y\), then there exists an OPRAM that has O(x) simulation overhead when consuming the same number of CPUs as the orginal PRAM for simulating at PRAM step.
In the interest of space, we defer the proof of this simple fact to the online full version [7].
2.3 Oblivious Hashing Scheme
Without loss of generality, we define only the parallel version, since the sequential version can be thought of the parallel version subject to executing on a single CPU.
A parallel oblivious hashing scheme contains the following two parallel, possibly randomized algorithms to be executed on a Concurrent Read, Exclusive Write PRAM:

\(\mathsf{T}\leftarrow \mathsf{Build}(1^\lambda , \{(k_i, v_i) \  \ \mathsf{dummy} \}_{i \in [n]})\): given a security parameter \(1^\lambda \), and a set of n elements, where each element is either a dummy denoted \(\mathsf{dummy} \) or a (key, value) pair denoted \((k_i, v_i)\), the \(\mathsf{Build}\) algorithm outputs a memory data structure denoted \(\mathsf{T}\) that will later facilitate query. For an input array \(S := \{(k_i, v_i) \  \ \mathsf{dummy} \}_{i \in [n]}\) to be valid, we require that any two nondummy elements in S must have distinct keys.

\(v \leftarrow \mathsf{Lookup}(\mathsf{T}, k)\): takes in the data structure \(\mathsf{T}\) and a (possibly dummy) query k, outputs a value v.
Correctness. Correctness is defined in a natural manner: given a valid initial set \(S := \{(k_i, v_i) \  \ \mathsf{dummy} \}_{i \in [n]}\) and a query k, we say that v is the correct answer for k with respect to S, iff

If \(k = \mathsf{dummy} \) (i.e., if k is a dummy query) or if \(k \notin S\), then \(v = \bot \).

Else, it must hold that \((k, v) \in S\).
More informally, the answer to any dummy query must be \(\bot \); if a query searches for an element nonexistent in S, then the answer must be \(\bot \). Otherwise, the answer returned must be consistent with the initial set S.
We say that a parallel oblivious hashing scheme is correct, if for any valid initial set S, for any query k, and for all \(\lambda \), it holds that
where the probability space is taken over the random coins chosen by the \(\mathsf{Build}\) and \(\mathsf{Lookup}\) algorithms.
Obliviousness. A query sequence \(\mathbf{k} = (k_1, \ldots , k_j)\) is said to be nonrecurrent, if all nondummy queries in \(\mathbf{k}\) are distinct.
A parallel hashing scheme denoted (Build, Lookup) is said to be oblivious, if there exists a polynomialtime simulator \({\mathsf {Sim}} \), such that for any security parameter \(\lambda \), for any valid initial set S, for any nonrecurrent query sequence \(\mathbf{k} := (k_1, \ldots , k_j)\) of polynomial length, it holds that
where \(\overset{c}{\equiv }\) denotes computationally indistinguishability, i.e., a computationally bounded adversary can distinguish between the two distributions with an advantage at most \(\mathsf{negl}(\lambda )\). Intuitively, this security definition says that a simulator, knowing only the length of the input set and the number of queries, can simulate the memory access patterns.
Definition 1
( \((W_\mathrm{build}, T_\mathrm{build}, W_\mathrm{lookup}, T_\mathrm{lookup})\) parallel oblivious hashing scheme). Let \(W_\mathrm{build}(\cdot , \cdot )\), \(W_\mathrm{lookup}(\cdot , \cdot )\), \(T_\mathrm{build}(\cdot , \cdot )\), and \(T_\mathrm{lookup}(\cdot , \cdot )\) be functions in n and \(\lambda \). We say that \((\mathsf{Build}, \mathsf{Lookup})\) is a \((W_\mathrm{build}, T_\mathrm{build}, W_\mathrm{lookup}, T_\mathrm{lookup} )\)parallel oblivious hashing scheme, iff \((\mathsf{Build}, \mathsf{Lookup})\) satisfies correctness and obliviousness as defined above; and moreover, the scheme achieves the following performance:

Building a hash table with n elements takes \(n \cdot W_\mathrm{build}(n, \lambda )\) total work and \(T_\mathrm{build}(n, \lambda )\) time with all but \(\mathsf{negl}(\lambda )\) probability. Note that \(W_\mathrm{build}(n, \lambda )\) is the perelement amount of work required for preprocessing.

A lookup query takes \(W_\mathrm{lookup}(n, \lambda )\) total work and \(T_\mathrm{lookup}(n, \lambda )\) time.
As a special case, we say that \((\mathsf{Build}, \mathsf{Lookup})\) is a \((W_\mathrm{build}, W_\mathrm{lookup})\)oblivious hashing scheme, if it is a \((W_\mathrm{build}, \_, W_\mathrm{lookup}, \_)\)parallel oblivious hashing scheme for any choice of the wildcard field “\(\_\)”—in other words, in the sequential case, we do not care about the scheme’s parallel runtime, and the scheme’s total work is equivalent to the runtime when running on a single CPU.
[Readonly lookup assumption.] When used in ORAM, observe that elements are inserted in a hash table in a batch only in the \(\mathsf{Build}\) algorithm. Moreover, we will assume that the \(\mathsf{Lookup}\) algorithm is readonly, i.e., it does not update the hash table data structure \(\mathsf{T}\), and no state is carried across between multiple invocations of \(\mathsf{Lookup}\).
A note on the security parameter. Since later in our application, we will need to apply oblivious hashing to different choices of n (including possibly small choices of n), throughout the description of the oblivious hashing scheme, we distinguish the security parameter denoted \(\lambda \) and the size of the set to be hashed denoted n.
2.4 Building Blocks
Duplicate suppression. Informally, duplicate suppression is the following building block: given an input array X of length n consisting of (key, value) pairs and possibly dummy elements where each key can have multiple occurrences, and additionally, given an upper bound \(n'\) on the number of distinct keys in X, the algorithm outputs a duplicatesuppressed array of length \(n'\) where only one occurrence of each key is preserved, and a preference function priority is used to choose which one.
Earlier works have [5, 19, 20] proposed an algorithm that relies on oblivious sorting to achieve duplicate suppression in \(O(n \log n)\) work and \(O(\log n)\) parallel runtime where \(n := X\).
Oblivious select. \(\mathsf{Select}(X, k, \mathsf{priority})\) takes in an array X where each element is either of the form (k, v) or a dummy denoted \(\bot \), a query k, and a priority function \(\mathsf{priority}\) which defines a total ordering on all elements with the same key; and outputs a value v such that \((k, v) \in X\) and moreover there exists no \((k, v') \in X\) such that \(v'\) is preferred over v for the key k by the priority function \(\mathsf{priority}\).
Oblivious select can be accomplished using a simple treebased algorithm [9] in \(O(\log n)\) parallel runtime and O(n) total work where \(n = X\).
Oblivious multicast. Oblivious multicast is the following building block. Given the following inputs:

a source array \(X := \{(k_i, v_i) \  \ \mathsf{dummy} \}_{i \in [n]}\) where each element is either of the form (k, v) or a dummy denoted \(\mathsf{dummy} \), and further all real elements must have a distinct k; and

a destination array \(Y := \{k'_i\}_{i \in [n]}\) where each element is a query \(k'\) (possibly having duplicates).
the oblivious multicast algorithm outputs an array \(\mathsf{ans} := \{v_i\}_{i \in [n]}\) such that if \(k'_i \notin X\) then \(v_i := \bot \); else it must hold that \((k'_i, v_i) \in X\).
Boyle et al. [5] propose an algorithm based on O(1) oblivious sorts that achieves oblivious multicast in \(O(\log n)\) parallel runtime and \(O(n \log n)\) total work.
Range prefix sum. We will rely on a parallel range prefix sum algorithm which offers the following abstraction: given an input array \(X = (x_1, \ldots , x_n)\) of length n where each element of X is of the form \(x_i := (k_i, v_i)\), output an array \(Y = (y_1, \ldots , y_n)\) where each \(y_i\) is defined as follows:

Let \(i' \le i\) be the smallest index such that \(k_{i'} = k_{i' + 1} = \ldots = k_i\);

\(y_i := \sum _{j = i'}^i v_{j}\).
In the GraphSC work, Nayak et al. [32] provide an oblivious algorithm that computes the range prefix sum in \(O(\log n)\) parallel runtime and \(O(n \log n)\) total work—in particular [32] defines a building block called “longest prefix sum” which is a slight variation of the range prefix sum abstraction we need. It is easy to see that Nayak et al.’s algorithm for longest prefix sum can be modified in a straightforward manner to compute our notion of range prefix sum.
3 Oblivious TwoTier Hashing Scheme
In this section, we present a simple oblivious twotier hashing scheme. Before we describe our scheme, we make a couple important remarks that the reader should keep in mind:

Note that our security definition implies that the adversary can only observe the memory access patterns, and we require simulatability of the memory access patterns. Therefore our scheme description does not explicitly encrypt data. When actually deploying an ORAM scheme, all data must be encrypted if the adversary can also observe the contents of memory.

In our oblivious hashing scheme, we use \(\lambda \) to denote the security parameter, and use n to denote the hash table’s size. Our ORAM application will employ hash tables of varying sizes, so n can be small. Observe that an instance of hash table building can fail with \(\mathsf{negl}(\lambda )\) probability; when this happens in the context of ORAM, the hash table building is restarted. This ensures that the ORAM is always correct, and the security parameter is related to the running time of the ORAM.

For small values of n, we need special treatment to obtain \(\mathsf{negl}(\lambda )\) security failure probability—specifically, we simply employ normal ballsandbins hashing for small values of n. Instead of having the ORAM algorithm deal with this issue, we wrap this part inside the oblivious hashing scheme, i.e., the oblivious hashing scheme will automatically decide whether to employ normal hashing or twotier hashing depending on n and \(\lambda \). This modular approach makes our ORAM and OPRAM algorithms conceptually simple and crystallizes the security argument as well.
The goal of this section is to give an oblivious hashing scheme with the following guarantee.
Theorem 1
(Parallel oblivious hashing). For any constant \(\epsilon > 0.5\), for any \(\alpha (\lambda ) := \omega (1)\), there exists a \((W_\mathrm{build}, T_\mathrm{build}, W_\mathrm{lookup}, T_\mathrm{lookup})\)parallel oblivious hashing scheme where
3.1 Construction: Nonoblivious and Sequential Version
For simplicity, we first present a nonoblivious and sequential version of the hashing algorithm, and we can use this version of the algorithm for the purpose of our stochastic analysis. Later in Sect. 3.2, we will show how to make the algorithm both oblivious and parallel. Henceforth, we fix some \(\epsilon \in (0.5, 1)\).
Case 1: \(n < e^{3 \log ^\epsilon \lambda }\). When n is sufficiently small relative to the security parameter \(\lambda \), we simply apply normal hashing (i.e., balls and bins) in the following manner. Let each bin’s capacity \(Z(\lambda ) = \alpha \log \lambda \), for any \(\alpha = \omega (1)\) superconstant function in \(\lambda \).
For building a hash table, first, generate a secret PRF key denoted \(\mathsf{sk}{\overset{\$}{\leftarrow }} \{0, 1\}^\lambda \). Then, store the n elements in \(B := \lceil 5n/Z \rceil \) bins each of capacity Z, where each element \((k, \_)\) is assigned to a pseudorandom bin computed as follows:
Due to a simple application of the Chernoff bound, the probability that any bin overflows is negligible in \(\lambda \) as long as Z is superlogarithmic in \(\lambda \).
To look up an element with the key k, compute the bin number as above and read the entire bin.
Case 2: \(n \ge e^{3 \log ^\epsilon \lambda }\). This is the more interesting case, and we describe our twotier hashing algorithm below.

Parameters and data structure. Suppose that our memory is organized into two hash tables named \(\mathsf{H}_1\) and \(\mathsf{H}_2\) respectively, where each hash table has \(B := \lceil \frac{n}{\log ^\epsilon \lambda } \rceil \) bins, and each bin can store at most \(Z := 5 \log ^\epsilon \lambda \) blocks.

\(\mathsf{Build}(1^\lambda , \{(k_i, v_i) \  \ \mathsf{dummy} \}_{i \in [n]})\):

a)
Generate a PRF key \(\mathsf{sk}{\overset{\$}{\leftarrow }} \{0, 1\}^\lambda \).

b)
For each element \((k_i, v_i) \in S\), try to place the element into the bin numbered \(\mathsf{PRF}_{\mathsf{sk}}(1k_i)\) in the firsttier hash table \(\mathsf{H}_1\). In case the bin is full, instead place the element in the overflow pile henceforth denoted \(\mathsf{Buf}\).

c)
For each element (k, v) in the overflow pile \(\mathsf{Buf}\), place the element into the bin numbered \(\mathsf{PRF}_{\mathsf{sk}}(2k)\) in the secondtier hash table \(\mathsf{H}_2\).

d)
Output \(\mathsf{T}:= (\mathsf{H}_1, \mathsf{H}_2, \mathsf{sk})\).

a)

\(\mathsf{Lookup}(\mathsf{T}, k)\): Parse \(\mathsf{T}:= (\mathsf{H}_1, \mathsf{H}_2, \mathsf{sk})\) and perform the following.

a)
If \(k = \bot \), i.e., this is a dummy query, return \(\bot \).

b)
Let \(i_1 := \mathsf{PRF}_{\mathsf{sk}}(1k)\). If an element of the form (k, v) is found in \(\mathsf{H}_1[i_1]\), return v. Else, let \(i_2 := \mathsf{PRF}_{\mathsf{sk}}(2k)\), look for an element of the form (k, v) in \(\mathsf{H}_2[i_2]\) and return v if found.

c)
If still not found, return \(\bot \).

a)
Overflow event. If in the above algorithm, an element happens to choose a bin in the secondtier hash table \(\mathsf{H}_2\) that is full, we say that a bad event called overflow has happened. When a hash building is called in the execution of an ORAM, recall that if an overflow occurs, we simply discard all work thus far and restart the build algorithm from the beginning.
In Sect. 3.4, we will prove that indeed, overflow events occur with negligible probability. Therefore, henceforth in our ORAM presentation, we will simply pretend that overflow events never happen during hash table building.
Remark 1
Since the oblivious hashing scheme is assumed to retry from scratch upon overflows, we guarantee perfect correctness and computational security failure (due to the use of a PRF). Similarly, our resulting ORAM and OPRAM schemes will also have perfect correctness and computational security. Obviously, the algorithms may execute longer if overflows and retries take place—henceforth in the paper, whenever we say that an algorithm’s total work or runtime is bounded by x, we mean that it is bounded by x except with negligible probability over the randomized execution.
3.2 Construction: Making It Oblivious
Oblivious Building. To make the building phase oblivious, it suffices to have the following \(\mathsf{Placement}\) building block.
Let B denote the number of bins, let Z denote each bin’s capacity, and let R denote the maximum capacity of the overflow pile. \(\mathsf{Placement}\) is the following building block. Given an array \(\mathsf{Arr} = \{(\mathsf{elem}_i, \mathsf{pos}_i) \  \ \mathsf{dummy} \}_{i \in [n]}\) containing n possibly dummy elements, where each nondummy element \(\mathsf{elem}_i\) is tagged with a pseudorandom bin number \(\mathsf{pos}_i \in [B]\), output B arrays \(\{\mathsf{Bin}_i\}_{i \in [B]}\) each of size exactly Z and an overflow pile denoted \(\mathsf{Buf}\) of size exactly R. The placement algorithm must output a valid assignment if one exists. Otherwise if no valid assignment exists, the algorithm should abort outputting hashfailure.
We say that an assignment is valid if the following constraints are respected:

(i)
Every nondummy \((\mathsf{elem}_i, \mathsf{pos}_i)\in \mathsf{Arr}\) exists either in some bin or in the overflow pile \(\mathsf{Buf}\).

(ii)
For every \(\mathsf{Bin}_i\), every nondummy element in \(\mathsf{Bin}_i\) is of the form \((\_, i)\). In other words, nondummy elements can only reside in their targeted bin or the overflow pile \(\mathsf{Buf}\).

(iii)
For every \(\mathsf{Bin}_i\), if there exists a dummy element in \(\mathsf{Bin}_i\), then no element of the form \((\_, i)\) appears in \(\mathsf{Buf}\). In other words, no elements from each bin should overflow to \(\mathsf{Buf}\) unless the bin is full.
[Special case]. A special case of the placement algorithm is when the overflow pile’s targeted capacity \(R = 0\). This special case will be used when we create the secondtier hash table.
Below, we show that using standard oblivious sorting techniques [2], Placement can be achieved in \(O(n \log n)\) total work:

1.
For each \(i \in [B]\), add Z copies of filler elements \((\diamond , i)\) where \(\diamond \) denotes that this is a filler element. These filler elements are there to make sure that each bin is assigned at least Z elements. Note that filler elements and dummy elements are treated differently.

2.
Oblivious sort all elements by their bin number. For elements with the same bin number, break ties by placing real elements to the left of filler elements.

3.
In a single linear scan, for each element that is not among the first Z elements of its bin, tag the element with the label “excess”.

4.
Oblivious sort all elements by the following ordering function:

All dummy elements must appear at the very end;

All nonexcess elements appear before excess elements;

For two nonexcess elements, the one with the smaller bin number appears first (breaking ties arbitrarily).

For excess elements, place real elements to the left of filler elements.

Oblivious lookups. It remains to show how to make lookup queries oblivious. To achieve this, we can adopt the following simple algorithm:

If the query \(k \ne \bot \): compute the firsttier bin number as \(i_1 := \mathsf{PRF}_{\mathsf{sk}}(1k)\). Read the entire bin numbered \(i_1\) in the firsttier hash table \(\mathsf{H}_1\). If found, read an entire random bin in \(\mathsf{H}_2\); else compute \(i_2 := \mathsf{PRF}_{\mathsf{sk}}(2k)\) and read the entire bin numbered \(i_2\) in the secondtier hash table \(\mathsf{H}_2\). Finally, return the element found or \(\bot \) if not found.

If the query \(k = \bot \), read an entire random bin in \(\mathsf{H}_1\), and an entire random bin in \(\mathsf{H}_2\). Both bin numbers are selected freshly and independently at random. Finally, return \(\bot \).
3.3 Construction: Making It Parallel
To make the aforementioned algorithm parallel, it suffices to make the following observations:

(i)
Oblivious sorting of n elements can be accomplished using a sorting circuit [2] that involves \(O(n \log n)\) total work and \(O(\log n)\) parallel runtime.

(ii)
Step 3 of the oblivious building algorithm involves a linear scan of the array marking each excessive element that exceeds its bin’s capacity. This linear scan can be implemented in parallel using the oblivious “range prefix sum” algorithm in \(O(n \log n)\) total work and \(O(\log n)\) parallel runtime. We refer the reader to Sect. 2.4 for a definition of the range prefix sum algorithm.

(iii)
Finally, observe that the oblivious lookup algorithm involves searching in entire bin for the desired block. This can be accomplished obliviously and in parallel through our “oblivious select” building block defined in Sect. 2.4. Since each bin’s capacity is \(O(\log ^\epsilon n)\), the oblivious select algorithm can be completed in \(O(\log \log n)\) parallel runtime and tight total work.
Remark 2
(The case of small n). So far, we have focused our attention on the (more interesting) case when \(n \ge e^{3\log ^\epsilon \lambda }\). When \(n < e^{3\log ^\epsilon \lambda }\), we rely on normal hashing, i.e., balls and bins. In this case, hash table building can be achieved through a similar parallel oblivious algorithm that completes in \(O(n \log n)\) total work and \(O(\log n)\) parallel runtime; further, each lookup query completes obliviously in \(O(\alpha \log \lambda )\) total work and \(O(\log \log \lambda )\) parallel runtime.
Performance of our oblivious hashing scheme. In summary, the resulting algorithm achieves the following performance:

Building a hash table with n elements takes \(O(n \log n)\) total work and \(O(\log n)\) parallel runtime with all but \(\mathsf{negl}(\lambda )\) probability, regardless of how large n is.

Each lookup query takes \(O(\log ^\epsilon \lambda )\) total work when \(n \ge e^{3\log ^\epsilon \lambda }\) and \(O(\alpha \log \lambda )\) total work when \(n < e^{3\log ^\epsilon \lambda }\) where \(\alpha (\lambda ) = \omega (1)\) can be any superconstant function. Further, regardless of how large n is, each lookup query can be accomplished in \(O(\log \log \lambda )\) parallel runtime.
3.4 Overflow Analysis
We give the overflow analysis of the twotier construction in Sect. 3.1. We use the following variant of Chernoff Bound.
Fact 2
(Chernoff Bound for Binomial Distribution). Let X be a random variable sampled from a binomial distribution (with any parameters). Then, for any \(k \ge 2 E[X]\), \(Pr[X \ge k] \le e^{\frac{k}{6}}\).
Utilization of firsttier hash. Recall that the number of bins is \(B := \left\lceil \frac{n}{\log ^\epsilon \lambda } \right\rceil \). For \(i \in [B]\), let \(X_i\) denote the number of items that are sent to bin i in the firsttier hash. Observe that the expectation \(E[X_i] = \frac{n}{B} \ge \log ^\epsilon \lambda \).
Overflow from firsttier hash. For \(i \in [B]\), let \(\widehat{X}_i\) be the number of items that are sent to bin i in the firsttier but have to be sent to the overflow pile because bin i is full. Recall that the capacity of a bin is \(Z := 5 \log ^\epsilon \lambda \). Then, it follows that \(\widehat{X}_i\) equals \(X_i  Z\) if \(X_i > Z\), and 0 otherwise.
Tail bound for overflow pile. We next use the standard technique of moment generating function to give a tail inequality for the number \(\sum _i \widehat{X}_i\) of items in the overflow pile. For sufficiently small \(t > 0\), we have
\(E[e^{t \widehat{X}_i}] \le 1 + \sum _{k \ge 1} \Pr [X_i = Z + k] \cdot e^{tk} \le 1 + \sum _{k \ge 1} \Pr [X_i \ge Z + k] \cdot e^{tk} \le 1 + \frac{\exp (\frac{Z}{6})}{e^{\frac{1}{6}  t}  1}\), where the last inequality follows from Fact 2 and a standard computation of a geometric series. For the special case \(t = \frac{1}{12}\), we have \(E[e^{\frac{\widehat{X}_i}{12}}] \le 1 + 12 \exp (\frac{Z}{6})\).
Lemma 1
(Tail Inequality for Overflow Pile). For \(k \ge 288 B e^{\frac{Z}{6}}\), \(\Pr [\sum _{i \in [B]} \widehat{X}_i \ge k] \le e^{\frac{k}{24}}\).
Proof
Fix \(t := \frac{1}{12}\). Then, we have \(\Pr [\sum _{i \in [B]} \widehat{X}_i \ge k] = \Pr [t\sum _{i \in [B]} \widehat{X}_i \ge tk] \le e^{t k} \cdot E[e^{t\sum _{i \in [B]} \widehat{X}_i}]\), where the last inequality follows from the Markov’s inequality.
As argued in [13], when n balls are thrown independently into n bins uniformly at random, then the numbers \(X_i\)’s of balls received in the bins are negatively associated. Since \(\widehat{X}_i\) is a monotone function of \(X_i\), it follows that the \(\widehat{X}_i\)’s are also negatively associated. Hence, it follows that \(E[e^{t\sum _{i \in [B]} \widehat{X}_i}] \le \prod _{i \in [B]} E[e^{t \widehat{X}_i}] \le \exp (12 B e^{\frac{Z}{6}})\).
Finally, observing that \(k \ge 288 B e^{\frac{Z}{6}}\), we have \(\Pr [\sum _{i \in [B]} \widehat{X}_i \ge k] \le \exp (12 B e^{\frac{Z}{6}}  \frac{k}{12}) \le e^{\frac{k}{24}}\), as required.
In view of Lemma 1, we consider \(N := 288 B e^{\frac{Z}{6}}\) as an upper bound on the number of items in the overflow pile. The following lemma gives an upper bound on the probability that a particular bin overflows in the secondtier hash.
Lemma 2
(Overflow Probability in the SecondTier Hash). Suppose the number of items in the overflow pile is at most \(N := 288 B e^{\frac{Z}{6}}\), and we fix some bin in the secondtier hash. Then, the probability that this bin receives more than Z items in the second tier hash is at most \(e^{\frac{Z^2}{6}}\).
Proof
Observe that the number of items that a particular bin receives is stochastically dominated by a binomial distribution with N items and probability \(\frac{1}{B}\). Hence, the probability that it is at least Z is at most \({N \atopwithdelims ()Z} \cdot (\frac{1}{B})^Z \le (\frac{Ne}{Z})^Z \cdot (\frac{1}{B})^Z \le e^{\frac{Z^2}{6}}\), as required.
Corollary 1
(Negligible Overflow Probability). Suppose the number n of items is chosen such that both \(B e^{\frac{Z}{6}}\) and \(Z^2\) are \(\omega (\log \lambda )\), where \(B := \left\lceil \frac{n}{\log ^\epsilon \lambda } \right\rceil \) and \(Z := \left\lceil 5 \log ^\epsilon \lambda \right\rceil \). Then, the probability that the overflow event happens in the secondtier hash is negligible in \(\lambda \).
Proof
Recall that \(B = \lceil \frac{n}{\log ^\epsilon \lambda }\rceil \), where \(n \ge e^{3\log ^\epsilon \lambda }\) in Theorem 1. By choosing \(N = 288 B e^{\frac{Z}{6}}\), from Lemma 1, the probability that there are more than N items in the overflow pile is \(\exp (\varTheta (N))\), which is negligible in \(\lambda \).
Given that the number of items in the overflow pile is at most N, according to Lemma 2, the probability that there exists some bin that overflows in the secondtier hash is at most \(B e^{\frac{Z^2}{6}}\) by union bound, which is also negligible in \(\lambda \), because we assume \(B \le \mathrm {poly}(\lambda )\).
3.5 Obliviousness
If there is no overflow, for any valid input, \(\mathsf{Build}\) accesses fixed addresses. Also, \(\mathsf{Lookup}\) fetches a fresh pseudorandom bin for each dummy or nondummy request. Hence, the simulator is just running \(\mathsf{Build}\) and \(\mathsf{Lookup}\) with all dummy requests. See the online full version [7] for the formal proof.
4 Modular Framework for Hierarchical ORAM
4.1 Preliminary: Hierarchical ORAM from Oblivious Hashing
Goldreich and Ostrovsky [19, 20] were the first to define Oblivious RAM (ORAM) and they provide an elegant solution to the problem which was since referred to as the “hierarchical ORAM”. Goldreich and Ostrovsky [19, 20] describe a specialcase instantiation of a hierarchical ORAM where they adopt an oblivious variant of naïve hashing. Their scheme was later extended and improved by several subsequent works [22, 26, 42].
In this section, we will present a generalized version of Goldreich and Ostrovsky’s hierarchical ORAM framework. Specifically, we will show that Goldreich and Ostrovsky’s core idea can be interpreted as the following: take any oblivious hashing scheme satisfying the abstraction defined in Sect. 2.3, we can construct a corresponding ORAM scheme that makes blackbox usage of the oblivious hashing scheme.
From our exposition, it will be clear why such a modular approach is compelling: it makes both the construction and the security proof simple. In comparison, earlier hierarchical ORAM works do not adopt this modular approach, and their conceptual complexity could sometimes confound the security proof [34].
Data structure. There are \(\log N + 1\) levels numbered \(0, 1, \ldots , L\) respectively, where \(L := \lceil \log _2 N \rceil \) is the maximum level. Each level is a hash table denoted \(\mathsf{T}_0, \mathsf{T}_{1}, \ldots , \mathsf{T}_L\) where \(\mathsf{T}_i\) has capacity \(2^i\). At any time, each table \(\mathsf{T}_i\) can be in two possible states, available or full. Available means that this level is currently empty and does not contain any blocks, and thus one can rebuild into this level. Full means that this level currently contains blocks, and therefore an attempt to rebuild into this level will effectively cause a cascading merge.
ORAM operations. Upon any memory access request \((\mathtt{read}, {\mathsf {addr}})\) or \((\mathtt{write}, {\mathsf {addr}}, {\mathsf {data}})\), perform the following procedure. For simplicity, we omit writing the security parameter of the algorithms, i.e., let \(\mathsf{Build}(\cdot ) := \mathsf{Build}(1^N, \cdot )\), and let \(\mathsf{Lookup}(\cdot ) := \mathsf{Lookup}(1^N, \cdot )\).

1.
\(\mathsf{found} := \mathtt{false}\).

2.
For each \(\ell = 0, 1, \ldots L\) in increasing order,

If not \(\mathsf{found}\), \({\mathsf {fetched}}:= \mathsf{Lookup}(\mathsf{T}_\ell , {\mathsf {addr}})\): if \({\mathsf {fetched}} \ne \bot \), let \(\mathsf{found} := \mathtt{true}\), \({\mathsf {data}} ^* := {\mathsf {fetched}} \).

Else \(\mathsf{Lookup}(\mathsf{T}_\ell , \bot )\).


3.
Let \(\mathsf{T}^\emptyset := \{({\mathsf {addr}}, {\mathsf {data}} ^*)\}\) if this is a \(\mathtt{read} \) operation; else let \(\mathsf{T}^\emptyset := \{({\mathsf {addr}}, {\mathsf {data}})\}\). Now perform the following hash table rebuilding:

Let \(\ell \) be the smallest level index such that \(\mathsf{T}_\ell \) is marked available. If all levels are marked full, then \(\ell := L\). In other words, \(\ell \) is the target level to be rebuilt.

Let \(S := \mathsf{T}^\emptyset \cup \mathsf{T}_0 \cup \mathsf{T}_{1} \cup \ldots \cup \mathsf{T}_{\ell  1}\); if all levels are marked full, then additionally let \(S := S \cup \mathsf{T}_L\). Further, tag each nondummy element in S with its level number, i.e., if a nondummy element in S comes from \(\mathsf{T}_i\), tag it with the level number i.

\(\mathsf{T}_{\ell } := \mathsf{Build}(\mathsf{SuppressDuplicate}(S, 2^\ell , \mathsf{pref}))\), and mark \(\mathsf{T}_\ell \) as full. Further, let \(\mathsf{T}_0 = \mathsf{T}_{1} = \ldots = \mathsf{T}_{\ell 1} := \emptyset \) and their status bits set to available. Here we adopt the following priority function \(\mathsf{pref}\):

When two or more real blocks with the same address (i.e., key) exist, the one with the smaller level number is preferred (and the algorithm maintains the invariant that no two blocks with the same address and the same level number should exist).



4.
Return \({\mathsf {data}} ^*\).
Deamortization. In the context of hierarchical ORAM, a hash table of capacity n is rebuilt every n memory requests, and we typically describe the ORAM’s overhead in terms of the amortized cost per memory request. As one may observe, every now and then, the algorithm needs to rebuild a hash table of size N, and thus a small number of memory requests may incur superlinear cost to complete.
A standard deamortization technique was described by Ostrovsky and Shoup [33] to evenly spread the cost of hash table rebuilding over time, and this deamortization framework only blows up the total work of the ORAM scheme by a small constant factor; the details are in the online full version [7]. In the rest of the paper, we assume that every instance of hash table used in an ORAM scheme is rebuilt in the background using this deamortization technique without explicitly mentioning so. Further, the stated costs in the theorems are applicable to worstcase performance (not just amortized).
Obliviousness. To show obliviousness of the above construction, we make the following observations.
Fact 3
(Nonrecurrent queries imply obliviousness). In the aforementioned ORAM construction, as long as lookup queries to every instance of hash table satisfies the nonrecurrent condition specified in Sect. 2.3, the resulting ORAM scheme satisfies obliviousness.
The proof of this fact is deferred to our online full version [7].
Fact 4
(Nonrecurrence condition is preserved). In the above ORAM construction, it holds that for every hash table instance, all lookup queries it receives satisfy the nonrecurrence condition.
Proof
Due to our ORAM algorithm, every \(2^\ell \) operations, the old instance of hash table \(\mathsf{T}_\ell \) is destroyed and a new hash table instance is created for \(\mathsf{T}_\ell \). It suffices to prove the nonrecurrence condition in between every two rebuilds for \(\mathsf{T}_\ell \). Suppose that after \(\mathsf{T}_\ell \) is rebuilt in some step, now we focus on the time steps going forward until the next rebuild. Consider when a block \(\mathsf{block}^*\) is first found in \(\mathsf{T}_\ell \) where \(\ell \in [L]\), \(\mathsf{block}^*\) is entered into \(\mathsf{T}^\emptyset \). Due to the definition of the ORAM algorithm, until the next time \(\mathsf{T}_\ell \) is rebuilt, \(\mathsf{block}^*\) exists in some \(\mathsf{T}_{\ell '}\) where \(\ell ' < \ell \). Due to the way the ORAM performs lookups—in particular, we would look up a dummy element in \(\mathsf{T}_\ell \) if \(\mathsf{block}^*\) is found in a smaller level—we conclude that until \(\mathsf{T}_{\ell }\) is rebuilt, no lookup query will ever be issued again for \(\mathsf{block}^*\) to \(\mathsf{T}_\ell \).
Lemma 3
(Obliviousness). Suppose that the underlying hashing scheme satisfies correctness and obliviousness as defined in Sect. 2.3, then it holds that the above ORAM scheme satisfies obliviousness as defined in Sect. 2.2.
Proof
Straightforward from Facts 3 and 4.
Theorem 2
(Hierarchical ORAM from oblivious hashing). Assume the existence of oneway functions and a \((W_\mathrm{build}, W_\mathrm{lookup})\)oblivious hashing scheme. Then, there exists an ORAM scheme that achieves the following blowup for block sizes of \(\varOmega (\log N)\) bits:
This theorem is essentially proved by Goldreich and Ostrovsky [19, 20]—however, they proved it only for a special case. We generalize their hierarchical ORAM construction and express it modularly to work with any oblivious hashing scheme as defined in Sect. 2.3.
Remark 3
We point out that due to the way we define our oblivious hashing abstraction, each instance of oblivious hash table will independently generate a fresh PRF key during \(\mathsf{Build}\), and this PRF key is stored alongside the resulting hash table data structure in memory. Throughout this paper, we assume that each PRF operation can be evaluated in O(1) runtime on top of our RAM. We stress that this implicit assumption (or equivalent) was made by all earlier ORAM works [19, 20, 22, 26] that rely on a PRF for security.
4.2 Preliminary: Improving Hierarchical ORAM by Balancing Reads and Writes
Subsequent to Goldreich and Ostrovsky’s groundbreaking result [19, 20], Kushilevitz et al. [26] propose an elegant optimization for the hierarchical ORAM framework such that under some special conditions to be specified later, they can shave a (multiplicative) \(\log \log N\) factor off the total work for a hierarchical ORAM scheme. Similarly, Kushilevitz et al. [26] describe a specialcase instantiation of an ORAM scheme based on oblivious Cuckoo hashing which was proposed by Goodrich and Mitzenmacher [22].
In this section, we observe that the Kushilevitz et al.’s idea can be generalized. For the sake of exposition, we will first ignore the smaller ORAM levels that employ normal hashing in the following discussion, i.e., we assume that the smaller levels that employ normal hashing will not be a dominating factor in the cost. Now, imagine that there is an oblivious hashing scheme such that for sufficiently large n, the perelement cost for preprocessing is more expensive than the cost of a lookup by a \(\log ^\delta n\) factor for some constant \(\delta > 0\). In other words, imagine that there exists a constant \(\delta > 0\) such that the following condition is met for sufficiently large n:
If the underlying oblivious hashing scheme satisfies the above condition, then Kushilevitz et al. [26] observes that Goldreich and Ostrovsky’s hierarchical ORAM construction is suboptimal in the sense that the cost of fetch phase is asymptotically smaller than the cost of the rebuild phase. Hence, the resulting ORAM’s total work will be dominated by the rebuild phase, which is then determined by the building cost of the underlying hashing scheme, i.e., \(W_\mathrm{build}(n, \lambda )\).
Having observed this, Kushilevitz et al. [26] propose the following modification to Goldreich and Ostrovsky’s hierarchical ORAM [19, 20]. In Goldreich and Ostrovsky’s ORAM, each level is a factor of 2 larger than the previous level—henceforth the parameter 2 is referred to the branching factor. Kushilevitz et al. [26] proposes to adopt a branching factor of \(\mu := \log N\) instead of 2, and this would reduce the number of levels to \(O(\log N/\log \log N)\)—in this paper, we will adopt a more general choice of \(\mu := \log ^\phi N\) for a suitable positive constant \(\phi \). To make this idea work, they allow up to \(\mu  1\) simultaneous hash table instances for any ORAM level. If for all levels below \(\ell \), all instances of hash tables are full, then all levels below \(\ell \) will be merged into a new hash table residing at level \(\ell + 1\). The core idea here is to balance the cost of the fetch phase and the rebuild phase by having a larger branching factor; and as an end result, we could shave a \(\log \log N\) factor from the ORAM’s total work.
We now elaborate on this idea more formally.
Data structure. Let \(\mu := \log ^\phi N\) for a suitable positive constant \(\phi \) to be determined later. There are \(O(\log N/\log \log N)\) levels numbered \(0, 1, \ldots , L\) respectively, where \(L = \lceil \log _\mu {N} \rceil \) denotes the maximum level. Except for level L, for every other \(\ell \in \{0, 1, \ldots , L1\}\): the \(\ell \)th level contains up to \(\mu  1\) hash tables each of capacity \(\mu ^\ell \). Henceforth we use the notation \(\mathsf{T}_\ell \) to denote level \(\ell \), and \(\mathsf{T}^i_\ell \) to denote the ith hash table within level \(\ell \). The largest level L contains a single hash table of capacity N denoted \(\mathsf{T}_L^0\). Finally, every level \(\ell \in \{0, 1, \ldots , L\}\) has a counter \(c_\ell \) initialized to 0. Effectively, for every level \(\ell \ne L\), if \(c_\ell = \mu 1\), then the level is considered full; else the level is considered available.
ORAM operations. Upon any memory access query \((\mathtt{read}, {\mathsf {addr}})\) or \((\mathtt{write}, {\mathsf {addr}}, {\mathsf {data}})\), perform the following procedure.

1.
\(\mathsf{found} := \mathtt{false}\).

2.
For each \(\ell = 0, 1, \ldots L\) in increasing order, for \(\tau = c_{\ell }1, c_\ell 2 \ldots 0\) in decreasing order:

If not \(\mathsf{found}\): \({\mathsf {fetched}}:= \mathsf{Lookup}(\mathsf{T}^{\tau }_\ell , {\mathsf {addr}})\); if \({\mathsf {fetched}} \ne \bot \), let \(\mathsf{found} := \mathtt{true}\), \({\mathsf {data}} ^* := {\mathsf {fetched}} \). Else \(\mathsf{Lookup}(\mathsf{T}^{\tau }_\ell , \bot )\).


3.
Let \(\mathsf{T}^\emptyset := \{({\mathsf {addr}}, {\mathsf {data}} ^*)\}\) if this is a \(\mathtt{read} \) operation; else let \(\mathsf{T}^\emptyset := \{({\mathsf {addr}}, {\mathsf {data}})\}\). Now, perform the following hash table rebuilding.

Let \(\ell \) be the smallest level index such that its counter \(c_{\ell } < \mu  1\). If no such level index exists, then let \(\ell := L\). In other words, we plan to rebuild a hash table in level \(\ell \).

Let \(S := \mathsf{T}^\emptyset \cup \mathsf{T}_0 \cup \mathsf{T}_{1} \cup \ldots , \cup \mathsf{T}_{\ell 1}\); and if \(\ell = L\), additionally, let \(S := S \cup \mathsf{T}_L^0\) and let \(c_L = 0\). Further, in the process, tag each nondummy element in S with its level number and its hash table number within the level. For example, if a nondummy element in S comes from \(\mathsf{T}^\tau _{i}\), i.e., the \(\tau \)th table in the ith level, tag it with \((i, \tau )\).

Let \(\mathsf{T}^{c_\ell }_\ell := \mathsf{Build}(\mathsf{SuppressDuplicate}(S, \mu ^\ell , \mathsf{pref}))\), and let \(c_\ell := c_\ell + 1\). Here we adopt the following priority function \(\mathsf{pref}\): when two or more blocks with the same address (i.e., key) exist, the one with the smaller level number is preferred; if there is a tie in level number, the one with the larger hash table number is preferred.

Let \(\mathsf{T}_0 = \mathsf{T}_{1} = \ldots = \mathsf{T}_{\ell 1} := \emptyset \) and set \(c_0 = c_{1} = \ldots = c_{\ell 1} := 0\).


4.
Return \({\mathsf {data}} ^*\).
Goldreich and Ostrovsky’s ORAM scheme [19, 20] is a special case of the above for \(\mu = 2\).
Deamortization. The deamortization technique of Ostrovsky and Shoup [33] (described in the online full version [7]) applies in general to hierarchical ORAM schemes for which each level is some data structure that is rebuilt regularly. Therefore, it can be applied to our scheme as well, and thus the work of rebuilding hash tables is spread evenly across memory requests.
Obliviousness. The obliviousness proof is basically identical to that presented in Sect. 4.1, since the only change here from Sect. 4.1 is that the parameters are chosen differently due to Kushilevitz et al.’s elegant idea [26].
Theorem 3
(Hierarchical ORAM variant.). Assume the existence of oneway functions and a \((W_\mathrm{build}, W_\mathrm{lookup})\)oblivious hashing scheme. Then, there exists an ORAM scheme that achieves the following blowup for block sizes of \(\varOmega (\log N)\) bits where \(L = O(\log N /\log \log N)\):
We note that Kushilevitz et al. [26] proved a special case of the above theorem, we now generalize their technique and describe it in the most general form.
4.3 Conceptually Simpler ORAM for Small Blocks
In the previous section, we presented a hierarchical ORAM scheme, reparametrized using Kushilevitz et al. [26]’s technique, consuming any oblivious hashing scheme with suitable performance characteristics as a blackbox.
To obtain a conceptually simple ORAM scheme with \(O(\log ^2 N/\log \log N)\) overhead, it suffices to plug in the oblivious twotier hashing scheme described earlier in Sect. 3.
Corollary 2
(Conceptually simpler ORAM for small blocks). There exists an ORAM scheme with \(O(\log ^2 N /\log \log N)\) runtime blowup for block sizes of \(\varOmega (\log N)\) bits.
Proof
Using the simple oblivious twotier hashing scheme in Sect. 3 with \(\epsilon = \frac{3}{4}\), we can set \(\phi = \frac{1}{4}\) in Theorem 3 to obtain the result.
4.4 IO Efficiency and the Case of Large CPU Cache
Besides the ORAM’s runtime, we often care about its IO performance as well, where IOcost is defined as the number of cache misses as in the standard externalmemory algorithms literature. When the CPU has a large amount of private cache, e.g., \(N^\epsilon \) blocks where \(\epsilon > 0\) is an arbitrarily small constant, several works have shown that oblivious sorting \(n \le N\) elements can be accomplished with O(n) IO operations [8, 21, 22]. Thus, a direct corollary is that for the case of \(N^\epsilon \) CPU cache, we can construct a computationally secure ORAM scheme with \(O(\log N)\) IOcost (by using the basic hierarchical ORAM construction with \(O(\log N)\) levels with an IOefficient oblivious sort).
5 Asymptotically Efficient OPRAM
In this section, we show how to construct an \(O(\frac{\log ^2 N}{\log \log N})\) OPRAM scheme. To do this, we will show how to parallelize our new \(O(\frac{\log ^2 N}{\log \log N})\)overhead ORAM scheme. Here we benefit tremendously from the conceptual simplicity of our new ORAM scheme. In particular, as mentioned earlier, our oblivious twotier hashing \((\mathsf{Build}, \mathsf{Lookup})\) algorithms have efficient parallel realizations. We will now present our OPRAM scheme. For simplicity, we first present a scheme assuming that the number of CPUs in each step of the computation is fixed and does not change over time. In this case, we show that parallelizing our earlier ORAM construction boils down to parallelizing the \((\mathsf{Build}\) and \(\mathsf{Lookup})\) algorithms of the oblivious hashing scheme. We then extend our construction to support the case when the number of CPUs varies over time.
5.1 Intuition
Warmup: uniform number of CPUs. We first describe the easier case of uniform m, i.e., the number of CPUs in the PRAM does not vary over time. Further, we will consider the simpler case when the branching factor \(\mu := 2\).

Data structure. Recall that our earlier ORAM scheme builds an exponentially growing hierarchy of oblivious hash tables, of capacities \(1, 2, 4, \ldots , N\) each. Here, we can do the same, but we can start the level of hierarchy at capacity \(m = 2^i\) (i.e., skip the smaller levels).

OPRAM operations. Given a batch of m simultaneous memory requests, suppose that all addresses requested are distinct—if not, we can run a standard conflict resolution procedure as described by Boyle et al. [5] incurring only \(O(\log m)\) parallel steps consuming m CPUs. We now need to serve these requests in parallel. In our earlier ORAM scheme, each request has two stages: (1) reading one block from each level of the exponentially growing hierarchy; and (2) perform necessary rebuilding of the levels. It is not hard to see that the fetch phase can be parallelized easily—particularly, observe that the fetch phase is readonly, and thus having m CPUs performing the reads in parallel will not lead to any write conflicts. It remains to show how to parallelize the rebuild phase. Recall that in our earlier ORAM scheme, each level has a status bit whose value is either available or full. Whenever we access a single block, we find the available (i.e., empty) level \(\ell \) and merge all smaller levels as well as the updated block into level \(\ell \). If no such level \(\ell \) exists, we simply merge all levels as well as the updated block into the largest level. Here in our OPRAM construction, since the smallest level is of size m, we can do something similar. We find the smallest available (i.e., empty) level \(\ell \), and merge all smaller levels as well as the possibly updated values of the m fetched blocks into level \(\ell \). If no such level \(\ell \) exists, we simply merge all levels as well as possibly updated values of the m fetched blocks into the largest level. Rebuilding a level in parallel effectively boils down to rebuilding a hash table in parallel (which boils down to performing O(1) number of oblivious sorts in parallel)—which we have shown to be possible earlier in Sect. 3.
Varying number of CPUs. Our definitions of PRAM and OPRAMs allow the number of CPUs to vary over time. In this case, oblivious simulation of a PRAM is more sophisticated. First, instead of truncating the smaller levels whose size are less than m, here we have to preserve all levels—henceforth we assume that we have an exponentially growing hierarchy with capacities \(1, 2, 4, \ldots , N\) respectively. The fetch phase is simple to parallelize as before, since the fetch phase does not make modifications to the data structure. We now describe a modified rebuild phase when serving a batch of \(m = 2^\gamma \) requests: note that in the following, \(\gamma \) is a level that matches the current batch size, i.e., the number of CPUs in the present PRAM step of interest:

(a)
Suppose level \(\gamma \) is marked available. Then, find the first available (i.e., empty) level \(\ell \) greater than \(\gamma \). Merge all levels below \(\gamma \) and the updated values of the newly fetched m blocks into level \(\ell \). If no such level \(\ell \) exists, then merge all blocks and the updated values of the newly fetched m blocks into the largest level L.

(b)
Suppose level \(\gamma \) is marked as full. Then, find the first available (i.e., empty) level \(\ell \) greater than \(\gamma \). Merge all levels below or equal to \(\gamma \) (but not the updated values of the m fetched blocks) into level \(\ell \); rebuild level \(\gamma \) to contain the updated values of the m fetched blocks. Similarly, if no such level \(\ell \) exists, then merge all blocks and the updated values of the newly fetched m blocks into the largest level L.
One way to view the above algorithm is as follows: let us view the concatenation of all levels’ status bits as a binary counter (where full denotes 1 and available denotes 0). If a single block is accessed like in the ORAM case, the counter is incremented, and if a level flips from 0 to 1, this level will be rebuilt. Further, if there would be a carryover to the \((L+1)\)st level, then the largest level L is rebuilt. However, now m blocks may be requested in a single batch—in this case, the above procedure for rebuilding effectively can be regarded as incrementing the counter by some value v where \(v \le 2m\)—in particular, the value v is chosen such that only O(1) levels must be rebuilt by the above rule.
We now embark on describing the full algorithm—specifically, we will describe for a general choice of the branching factor \(\mu \) that is not necessarily 2. Further, our description supports the case of varying number of CPUs.
5.2 Detailed Algorithm
Data structure. Same as in Sect. 4.2. Specifically, there are \(O(\log N/\log \log N)\) levels numbered \(0, 1, \ldots , L\) respectively, where \(L = \lceil \log _\mu {N} \rceil \) denotes the maximum level. Except for level L, for every other \(\ell \in \{0, 1, \ldots , L1\}\): the \(\ell \)th level contains up to \(\mu  1\) hash tables each of capacity \(\mu ^\ell \). Henceforth, we use the notation \(\mathsf{T}_\ell \) to denote level \(\ell \). Moreover, for \(0 \le i < \mu  1\), we use \(\mathsf{T}^i_\ell \) to denote the ith hash table within level \(\ell \). The largest level L contains a single hash table of capacity N denoted \(\mathsf{T}_L^0\). Finally, every level \(\ell \in \{0, 1, \ldots , L\}\) has a counter \(c_\ell \) initialized to 0.
We say that a level \(\ell < L\) is available if its counter \(c_\ell < \mu  1\); otherwise, \(c_\ell = \mu  1\), and we say that the level \(\ell < L\) is full. For the largest level L, we say that it is available if \(c_L = 0\); else we say that it is full. Note that for the case of general \(\mu > 2\), available does not necessarily mean that the level’s empty.
OPRAM operations. Upon a batch of m memory access requests \(Q := \{\mathsf {op} _p\}_{p \in [m]}\) where each \(\mathsf {op} _p\) is of the form \((\mathtt{read}, {\mathsf {addr}} _p)\) or \((\mathtt{write}, {\mathsf {addr}} _p, {\mathsf {data}} _p)\), perform the following procedure. Henceforth we assume that \(m = 2^{\gamma }\) where \(\gamma \) denotes the level whose capacity matches the present batch size.

1.
Conflict resolution. \(Q' := \mathsf{SuppressDuplicate}(Q, m, \mathsf{PRAM}.\mathsf{priority})\), i.e., perform conflict resolution on the batch of memory requests Q, and obtain a batch \(Q'\) of the same size but where each distinct address appears only once—suppressing duplicates using the \(\mathsf{PRAM}\)’s priority function \(\mathsf{priority}\), and padding the resulting set with dummies to length m.

2.
Fetch phase. For each \(\mathsf {op} _i \in Q'\) in parallel where \(i \in [m]\), parse \(\mathsf {op} _i = \bot \) or \(\mathsf {op} _i = (\mathtt{read}, {\mathsf {addr}} _i)\) or \(\mathsf {op} _i = (\mathtt{write}, {\mathsf {addr}} _i, {\mathsf {data}} _i)\):

(a)
If \(\mathsf {op} _i = \bot \), let \(\mathsf{found} := \mathtt{true}\); else let \(\mathsf{found} := \mathtt{false}\).

(b)
For each \(\ell = 0, 1, \ldots L\) in increasing order, for \(\tau = c_{\ell }1, c_\ell 2 \ldots 0\) in decreasing order:

If not \(\mathsf{found}\): \({\mathsf {fetched}}:= \mathsf{Lookup}(\mathsf{T}^{\tau }_\ell , {\mathsf {addr}} _i)\); if \({\mathsf {fetched}} \ne \bot \), let \(\mathsf{found} := \mathtt{true}\), \({\mathsf {data}} ^*_i := {\mathsf {fetched}} \).

Else, \(\mathsf{Lookup}(\mathsf{T}^{\tau }_\ell , \bot )\).


(a)

3.
Rebuild phase. For each \(\mathsf {op} _i \in Q'\) in parallel where \(i \in [m]\): if \(\mathsf {op} _i\) is a \(\mathtt{read} \) operation add \(({\mathsf {addr}} _i, {\mathsf {data}} ^*_i)\) to \(\mathsf{T}^\emptyset \); else if \(\mathsf {op} _i\) is a \(\mathtt{write} \) operation, add \(({\mathsf {addr}} _i, {\mathsf {data}} _i)\) to \(\mathsf{T}^\emptyset \); else add \(\bot \) to \(\mathsf{T}^\emptyset \). Perform the following hash table rebuilding—recall that \(\gamma \) is the level whose capacity matches the present batch size:

(a)
If level \(\gamma \) is full, then skip this step; else, perform the following: Let \(S := \mathsf{T}_0 \cup \mathsf{T}_1 \cup \ldots \cup \mathsf{T}_{\gamma 1}\), and \(\mathsf{T}_\gamma ^{c_\gamma } := \mathsf{Build}(\mathsf{SuppressDuplicate}(S, \mu ^\gamma , \mathsf{pref}))\) where \(\mathsf{pref}\) prefers a block from a smaller level (i.e., the fresher copy) if multiple blocks of the same address exists. Let \(c_\gamma := c_\gamma + 1\), and for every \(j < \gamma \), let \(c_j := 0\).

(b)
At this moment, if level \(\gamma \) is still available, then let \(\mathsf{T}_\gamma ^{c_\gamma } := \mathsf{Build}(\mathsf{T}^\emptyset )\), and \(c_\gamma := c_\gamma + 1\). Else, if level \(\gamma \) is full, perform the following: Find the first available level \(\ell > \gamma \) greater than \(\gamma \) that is available; if no such level \(\ell \) exists, let \(\ell := L\) and let \(c_L := 0\). Let \(S := \mathsf{T}^\emptyset \cup \mathsf{T}_0 \cup \ldots \cup \mathsf{T}_{\ell 1}\); if \(\ell = L\), additionally include \(S := S \cup \mathsf{T}_L\). Let \(\mathsf{T}_\ell ^{c_\ell } := \mathsf{Build}(\mathsf{SuppressDuplicate}(S, \mu ^\ell , \mathsf{pref}))\), and let \(c_\ell := c_\ell + 1\). For every \(j < \ell \), reset \(c_j := 0\).

(a)
Deamortization. The deamortization technique (described in the online full version [7]) of Ostrovsky and Shoup [33] applies here as well, and thus the work of rebuilding hash tables are spread evenly across memory requests.
Obliviousness. The obliviousness proof is basically identical to that presented in Sect. 4.1. Since we explicitly resolve conflict before serving a batch of m requests, we preserve the nonrecurrence condition. The only remaining differences here in comparison with Sect. 4.1 is that (1) here we use a general branching factor of \(\mu \) rather than 2 (as in Sect. 4.1); and (2) here we consider the parallel setting. It is clear that neither of these matter to the obliviousness proof.
Theorem 4
(OPRAM from oblivious parallel hashing). Assume the existence of oneway functions and a \((W_\mathrm{build}, T_\mathrm{build}, W_\mathrm{lookup}, T_\mathrm{lookup})\)oblivious hashing scheme. Then, there exists an ORAM scheme that achieves the following performance for block sizes of \(\varOmega (\log N)\) bits where \(L = O(\frac{\log N}{\log \log N})\):
Proof
Basically, the proof is our explicit OPRAM construction from any parallel oblivious hashing scheme described earlier in this section. For total work and parallel runtime blowup, we basically take the maximum of the ORAM’s fetch phase and rebuild phase. The additive term \(O(L\log N)\) in the total work stems from additional building blocks such as parallel duplicate suppression and other steps in our OPRAM scheme; and same for the additive term O(L) in the parallel runtime blowup.
Using the simple oblivious hashing scheme in Sect. 3 with \(\epsilon = \frac{3}{4}\), we can set \(\phi = \frac{1}{4}\) to obtain the following corollary.
Corollary 3
(Asympototically efficient OPRAM for small blocks). Assume that oneway functions exist. Then, there exists a computationally secure OPRAM scheme that achieves \(O(\log ^2 N/\log \log N)\) simulation overhead when the block size is at least \(\varOmega (\log N)\) bits.
Notes
 1.
 2.
Henceforth in this paper, we use n to denote the size of a hash table and \(\lambda \) to denote its security parameter. For our ORAM construction, we use N to denote both the logical memory size as well as the ORAM’s security parameter. This distinction is necessary since the ORAM will employ hash tables of varying n.
 3.
The title of their paper [31] suggests \(O(\log ^2 N)\) overhead, since they did not account for the cost of CPUCPU communication when describing the overhead.
References
Adler, M., Chakrabarti, S., Mitzenmacher, M., Rasmussen, L.E.: Parallel randomized load balancing. Random Struct. Algorithms 13(2), 159–188 (1998)
Ajtai, M., Komlós, J., Szemerédi, E.: An O(N Log N) sorting network. In: STOC (1983)
Asharov, G., Chan, H., Nayak, K., Pass, R., Ren, L., Shi, E.: Oblivious computation with data locality. IACR Cryptology ePrint Archive 2017/772 (2017)
Bast, H., Hagerup, T.: Fast and reliable parallel hashing. In: SPAA (1991)
Boyle, E., Chung, K.M., Pass, R.: Oblivious parallel RAM and applications. In: Kushilevitz, E., Malkin, T. (eds.) TCC 2016. LNCS, vol. 9563, pp. 175–204. Springer, Heidelberg (2016). https://doi.org/10.1007/9783662490990_7
Chan, T.H.H., Chung, K.M., Shi, E.: On the depth of oblivious parallel RAM. In: Asiacrypt (2017)
Chan, T.H.H., Guo, Y., Lin, W.K., Shi, E.: Oblivious hashing revisited, and applications to asymptotically efficient ORAM and OPRAM. Online full version of this paper, IACR Cryptology ePrint Archive 2017/924 (2017)
Chan, T.H.H., Guo, Y., Lin, W.K., Shi, E.: Cacheoblivious and dataoblivious sorting and applications. In: SODA (2018)
Chan, T.H.H., Shi, E.: Circuit OPRAM: unifying statistically and computationally secure ORAMs and OPRAMs. In: TCC (2017)
Chen, B., Lin, H., Tessaro, S.: In: TCC, pp. 205–234 (2016)
Chung, K.M., Liu, Z., Pass, R.: Statisticallysecure ORAM with \(\tilde{O}(\log ^2n)\) overhead. In: Asiacrypt (2014)
Dietzfelbinger, M., Karlin, A., Mehlhorn, K., Meyer auF der Heide, F., Rohnert, H., Tarjan, R.E.: Dynamic perfect hashing: upper and lower bounds. SIAM J. Comput. 23(4), 738–761 (1994)
Dubhashi, D.P., Ranjan, D.: Balls and bins: a study in negative dependence. Random Struct. Algorithms 13(2), 99–124 (1998)
Fletcher, C.W., Ren, L., Kwon, A., van Dijk, M., Devadas, S.: Freecursive ORAM: [nearly] free recursion and integrity verification for positionbased oblivious RAM. In: ASPLOS (2015)
Fletcher, C.W., Ren, L., Kwon, A., van Dijk, M., Stefanov, E., Devadas, S.: RAW Path ORAM: a lowlatency, lowarea hardware ORAM controller with integrity verification. IACR Cryptology ePrint Archive 2014/431 (2014)
Fletcher, C.W., Ren, L., Yu, X., van Dijk, M., Khan, O., Devadas, S.: Suppressing the oblivious RAM timing channel while making information leakage and program efficiency tradeoffs. In: HPCA, pp. 213–224 (2014)
Fredman, M.L., Komlós, J., Szemerédi, E.: Storing a sparse table with O(1) worst case access time. J. ACM 31(3), 538–544 (1984)
Gentry, C., Halevi, S., Lu, S., Ostrovsky, R., Raykova, M., Wichs, D.: Garbled RAM revisited. In: Nguyen, P.Q., Oswald, E. (eds.) EUROCRYPT 2014. LNCS, vol. 8441, pp. 405–422. Springer, Heidelberg (2014). https://doi.org/10.1007/9783642552205_23
Goldreich, O.: Towards a theory of software protection and simulation by oblivious RAMs. In: STOC (1987)
Goldreich, O., Ostrovsky, R.: Software protection and simulation on oblivious RAMs. J. ACM 43, 431–473 (1996)
Goodrich, M.T.: Dataoblivious externalmemory algorithms for the compaction, selection, and sorting of outsourced data. In: SPAA (2011)
Goodrich, M.T., Mitzenmacher, M.: Privacypreserving access of outsourced data via oblivious RAM simulation. In: Aceto, L., Henzinger, M., Sgall, J. (eds.) ICALP 2011. LNCS, vol. 6756, pp. 576–587. Springer, Heidelberg (2011). https://doi.org/10.1007/9783642220128_46
Gordon, S.D., Katz, J., Kolesnikov, V., Krell, F., Malkin, T., Raykova, M., Vahlis, Y.: Secure twoparty computation in sublinear (amortized) time. In: CCS (2012)
Hagerup, T.: Fast and optimal simulations between CRCW PRAMs. In: Finkel, A., Jantzen, M. (eds.) STACS 1992. LNCS, vol. 577, pp. 45–56. Springer, Heidelberg (1992). https://doi.org/10.1007/3540552103_172
Hagerup, T.: The logstar revolution. In: Finkel, A., Jantzen, M. (eds.) STACS 1992. LNCS, vol. 577, pp. 257–278. Springer, Heidelberg (1992). https://doi.org/10.1007/3540552103_189
Kushilevitz, E., Lu, S., Ostrovsky, R.: On the (in)security of hashbased oblivious RAM and a new balancing scheme. In: SODA (2012)
Liu, C., Wang, X.S., Nayak, K., Huang, Y., Shi, E.: ObliVM: a programming framework for secure computation. In S & P (2015)
Lu, S., Ostrovsky, R.: How to garble ram programs. In: Eurocrypt (2013)
Maas, M., Love, E., Stefanov, E., Tiwari, M., Shi, E., Asanovic, K., Kubiatowicz, J., Song, D.: Phantom: practical oblivious computation in a secure processor. In: CCS (2013)
Matias, Y., Vishkin, U.: Converting high probability into nearlyconstant time—with applications to parallel hashing. In: STOC, pp. 307–316 (1991)
Nayak , K., Katz, J.: An oblivious parallel ram with \(o(\log ^2n)\) parallel runtime blowup. https://eprint.iacr.org/2016/1141
Nayak, K., Wang, X.S., Ioannidis, S., Weinsberg, U., Taft, N., Shi, E.: GraphSC: parallel secure computation made easy. In: IEEE S & P (2015)
Ostrovsky, R., Shoup, V.: Private information storage (extended abstract). In: STOC, pp. 294–303 (1997)
Pinkas, B., Reinman, T.: Oblivious RAM revisited. In: Rabin, T. (ed.) CRYPTO 2010. LNCS, vol. 6223, pp. 502–519. Springer, Heidelberg (2010). https://doi.org/10.1007/9783642146237_27
Ren, L., Fletcher, C.W., Kwon, A., Stefanov, E., Shi, E., van Dijk, M., Devadas, S.: Constants count: practical improvements to oblivious RAM. In: USENIX Security Symposium, pp. 415–430 (2015)
Ren, L., Yu, X., Fletcher, C.W., van Dijk, M., Devadas, S.: Design space exploration and optimization of path oblivious RAM in secure processors. In: ISCA (2013)
Shi, E., Chan, T.H.H., Stefanov, E., Li, M.: Oblivious RAM with \(O((\log N)^3)\) worstcase cost. In: Asiacrypt (2011)
Stefanov, E., Shi, E.: Multicloud oblivious storage. In: CCS (2013)
Stefanov, E., Shi, E.: ObliviStore: high performance oblivious cloud storage. In: IEEE Symposium on Security and Privacy (S & P) (2013)
Stefanov, E., van Dijk, M., Shi, E., Fletcher, C., Ren, L., Yu, X., Devadas, S.: Path ORAM  an extremely simple oblivious ram protocol. In: CCS (2013)
Wang, X., Chan, H., Shi, E.: Circuit ORAM: on tightness of the GoldreichOstrovsky lower bound. In: CCS (2015)
Williams, P., Sion, R., Carbunar, B.: Building castles out of mud: practical access pattern privacy and correctness on untrusted storage. In: CCS, pp. 139–148 (2008)
Williams, P., Sion, R., Tomescu, A.: PrivateFS: a parallel oblivious file system. In: CCS (2012)
Acknowledgments
This work is supported in part by NSF grants CNS1314857, CNS1514261, CNS1544613, CNS1561209, CNS1601879, CNS1617676, an Office of Naval Research Young Investigator Program Award, a Packard Fellowship, a DARPA Safeware grant (subcontractor under IBM), a Sloan Fellowship, Google Faculty Research Awards, a Baidu Research Award, and a VMWare Research Award.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 International Association for Cryptologic Research
About this paper
Cite this paper
Chan, TH.H., Guo, Y., Lin, WK., Shi, E. (2017). Oblivious Hashing Revisited, and Applications to Asymptotically Efficient ORAM and OPRAM. In: Takagi, T., Peyrin, T. (eds) Advances in Cryptology – ASIACRYPT 2017. ASIACRYPT 2017. Lecture Notes in Computer Science(), vol 10624. Springer, Cham. https://doi.org/10.1007/9783319706948_23
Download citation
DOI: https://doi.org/10.1007/9783319706948_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 9783319706931
Online ISBN: 9783319706948
eBook Packages: Computer ScienceComputer Science (R0)