Delegating RAM Computations with Adaptive Soundness and Privacy

  • Prabhanjan Ananth
  • Yu-Chi Chen
  • Kai-Min Chung
  • Huijia Lin
  • Wei-Kai Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9986)


We consider the problem of delegating RAM computations over persistent databases. A user wishes to delegate a sequence of computations over a database to a server, where each computation may read and modify the database and the modifications persist between computations. Delegating RAM computations is important as it has the distinct feature that the run-time of computations maybe sub-linear in the size of the database.

We present the first RAM delegation scheme that provide both soundness and privacy guarantees in the adaptive setting, where the sequence of delegated RAM programs are chosen adaptively, depending potentially on the encodings of the database and previously chosen programs. Prior works either achieved only adaptive soundness without privacy [Kalai and Paneth, ePrint’15], or only security in the selective setting where all RAM programs are chosen statically [Chen et al. ITCS’16, Canetti and Holmgren ITCS’16].

Our scheme assumes the existence of indistinguishability obfuscation (\(\mathsf {i}\mathcal {O}\)) for circuits and the decisional Diffie-Hellman (DDH) assumption. However, our techniques are quite general and in particular, might be applicable even in settings where iO is not used. We provide a “security lifting technique” that “lifts” any proof of selective security satisfying certain special properties into a proof of adaptive security, for arbitrary cryptographic schemes. We then apply this technique to the delegation scheme of Chen et al. and its selective security proof, obtaining that their scheme is essentially already adaptively secure. Because of the general approach, we can also easily extend to delegating parallel RAM (PRAM) computations. We believe that the security lifting technique can potentially find other applications and is of independent interest.


Security Proof Security Game Malicious Server Delegation Scheme Adaptive Adversary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Yael Kalai for insightful discussions in the early stages of this project.

This work was done in part while the authors were visiting the Simons Institute for the Theory of Computing, supported by the Simons Foundation and by the DIMACS/Simons Collaboration in Cryptography through NSF grant CNS-1523467.

Prabhanjan Ananth is supported in part by grant #360584 from the Simons Foundation and supported in part from a DARPA/ARL SAFEWARE award, NSF Frontier Award 1413955, NSF grants 1228984, 1136174, 1118096, and 1065276. This material is based upon work supported by the Defense Advanced Research Projects Agency through the ARL under Contract W911NF-15-C-0205. The views expressed are those of the author and do not reflect the official policy or position of the Department of Defense, the National Science Foundation, or the U.S. Government.

Kai-Min Chung was partially supported by Ministry of Science and Technology, Taiwan, under Grant no. MOST 103-2221-E-001-022-MY3.

Huijia Lin was partially supported by NSF grants CNS-1528178 and CNS-1514526.


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Copyright information

© International Association for Cryptologic Research 2016

Authors and Affiliations

  • Prabhanjan Ananth
    • 1
  • Yu-Chi Chen
    • 2
  • Kai-Min Chung
    • 2
  • Huijia Lin
    • 3
  • Wei-Kai Lin
    • 4
  1. 1.Center for Encrypted FunctionalitiesUniversity of California Los AngelesLos AngelesUSA
  2. 2.Academia SinicaTaipeiTaiwan
  3. 3.University of CaliforniaSanta BarbaraUSA
  4. 4.Cornell UniversityIthacaUSA

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