Efficient secure computation optimization
Pages 11 - 18
Abstract
Secure computation has high computational resource requirements during run-time. Secure computation optimization can lower these requirements, but has high computational resource requirements during compile-time. This prevents automatic optimization of most larger secure computations. In this paper we present an efficient optimization algorithm that does no longer require the use of a theorem prover. For a secure computation with m statements of which n are branching statements we lower the complexity from O(2^(2^n) m) to O(m^5 2^n). Using an implementation of our algorithm we can extend automatic optimization to further examples such as the AES key schedule.
References
[1]
G. Aggarwal, N. Mishra, and B. Pinkas. Secure Computation of the k-th Ranked Element. In Advances in Cryptology (EUROCRYPT), 2004.
[2]
M. J. Atallah, F. Kerschbaum, and W. Du. Secure and Private Sequence Comparisons. In Proceedings of the ACM Workshop on Privacy in the Electronic Society (WPES), 2003.
[3]
D. Beaver, S. Micali, and P. Rogaway. The Round Complexity of Secure Protocols. In Proceedings of the 22nd ACM Symposium on Theory of Computing (STOC), 1990.
[4]
B. Beckert and R. Gore. System Description: leanK 2.0. In Proceedings of the International Conference on Automated Deduction (CADE), 1998.
[5]
A. Ben-David, N. Nisan, and B. Pinkas. FairplayMP: A System for Secure Multi-Party Computation. In Proceedings of the 15th ACM Conference on Computer and Communications Security (CCS), 2008.
[6]
D. Bogdanov, S. Laur, and J. Willemson. Sharemind: A Framework for Fast Privacy-Preserving Computations. In Proceedings of the 13th European Symposium on Research in Computer Security (ESORICS), 2008.
[7]
D. Bogdanov, R. Talviste, and J. Willemson. Deploying Secure Multi-Party Computation for Financial Data Analysis. In Proceedings of the 16th International Conference on Financial Cryptography and Data Security (FC), 2012.
[8]
P. Bogetoft, D. L. Christensen, I. Damgård, M. Geisler, T. P. Jakobsen, M. Krøigaard, J. D. Nielsen, J. B. Nielsen, K. Nielsen, J. Pagter, M. I. Schwartzbach, and T. Toft. Secure Multiparty Computation Goes Live. In Proceedings of the 13th International Conference on Financial Cryptography and Data Security (FC), 2009.
[9]
O. Catrina, and F. Kerschbaum. Fostering the Uptake of Secure Multiparty Computation in E-Commerce. in Proceedings of the International Workshop on Frontiers in Availability, Reliability and Security (FARES), 2008.
[10]
R. Cytron, J. Ferrante, B. K. Rosen, M. N. Wegman, and F. K. Zadeck. Efficiently computing static single assignment form and the control dependence graph. ACM Transactions Programming Languages and Systems, 13(4), 1991.
[11]
I. Damgård, M. Geisler, M. Krøigaard, and J. B. Nielsen. Asynchronous Multiparty Computation: Theory and Implementation. In Proceedings of the 12th International Conference on Practice and Theory in Public Key Cryptography (PKC), 2009.
[12]
E. De Cristofaro, and G. Tsudik. Practical Private Set Intersection Protocols with Linear Complexity. In Proceedings of the 14th International Conference on Financial Cryptography and Data Security (FC), 2010.
[13]
R. Floyd. Algorithm 97: Shortest Path. Communications of the ACM 5(6), 1962.
[14]
O. Goldreich. Foundations of Cryptography: Volume 2 -- Basic Applications. Cambridge University Press, 2004.
[15]
S. Goldwasser. Multi-Party Computations: Past and Present. In Proceedings of the 16th ACM Symposium on Principles of Distributed Computing (PODC), 1997.
[16]
J. Y. Halpern. Reasoning about knowledge: a survey. In D. M. Gabbay, C. J. Hogger, and J. A. Robinson, editors, Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 4.Oxford University Press, 1995.
[17]
W. Henecka, S. Kögl, A.-R. Sadeghi, T. Schneider, and I. Wehrenberg. Tasty: Tool for Automating Secure Two-partY computations. In Proceedings of the 17th ACM Conference on Computer and Communications Security (CCS), 2010.
[18]
Y. Huang, D. Evans, and J. Katz. Private Set Intersection: Are Garbled Circuits Better than Custom Protocols? In Proceedings of the 19th Annual Network and Distributed System Security Symposium (NDSS), 2012.
[19]
Y. Huang, D. Evans, J. Katz, and L. Malka. Faster secure two-party computation using garbled circuits. In Proceedings of the 20th USENIX Security Symposium, 2011.
[20]
Y. Huang, L. Malka, D. Evans, and J. Katz. Efficient Privacy-Preserving Biometric Identification. In Proceedings of the 18th Annual Network and Distributed System Security Symposium (NDSS), 2011.
[21]
Y. Ishai, J. Kilian, K. Nissim, and E. Petrank. Extending Oblivious Transfers Efficiently. In Advances in Cryptology (CRYPTO), 2003.
[22]
S. Jha, L. Kruger, and V. Shmatikov. Towards Practical Privacy for Genomic Computation. In Proceedings of the 29th IEEE Symposium on Security and Privacy (S&P), 2008.
[23]
F. Kerschbaum. Automatically Optimizing Secure Computation. In Proceedings of the 18th ACM Conference on Computer and Communications Security (CCS), 2011.
[24]
F. Kerschbaum. Expression Rewriting for Optimizing Secure Computation. In Proceedings of the 3rd ACM Conference on Data and Application Security and Privacy (CODASPY), 2013.
[25]
F. Kerschbaum. An Information-Flow Type-System for Mixed Protocol Secure Computation. In Proceedings of the 8th ACM Symposium on Information, Computer and Communications Secuirty (ASIACCS), 2013.
[26]
F. Kerschbaum, D. Dahlmeier, A. Schröpfer, and D. Biswas. On the Practical Importance of Communication Complexity for Secure Multi-Party Computation Protocols. In Proceedings of the 24th ACM Symposium on Applied Computing (SAC), 2009.
[27]
F. Kerschbaum, T. Schneider, and A. Schröpfer. Automatic Protocol Selection in Secure Two-Party Computations. In 20th Annual Network and Distributed System Security Symposium (NDSS), 2013
[28]
F. Kerschbaum, A. Schröpfer, A. Zilli, R. Pibernik, O. Catrina, S. de Hoogh, B. Schoenmakers, S. Cimato, and E. Damiani. Secure Collaborative Supply Chain Management. IEEE Computer, 44(9), 2011.
[29]
V. Kolesnikov, and T. Schneider. Improved Garbled Circuit: Free XOR Gates and Applications. In Proceedings of the 35th International Colloquium on Automata, Languages and Programming (ICALP), 2008.
[30]
Y. Lindell, and B. Pinkas. A Proof of Security of Yao's Protocol for Two-Party Computation. Journal of Cryptology 22(2), 2009.
[31]
D. Malkhi, N. Nisan, B. Pinkas, and Y. Sella. Fairplay -- A Secure Two-Party Computation System. In Proceedings of the 13th USENIX Security Symposium, 2004.
[32]
P. Paillier. Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. In Advances in Cryptology (EUROCRYPT), 1999.
[33]
B. Pinkas, T. Schneider, N. P. Smart, and S. C. Williams. Secure Two-Party Computation is Practical. In Advances in Cryptology (ASIACRYPT), 2009.
[34]
A. Rastogi, P. Mardziel, M. Hammer, and M. Hicks. Knowledge Inference for Optimizing Secure Multi-party Computation. In Proceedings of the ACM Workshop on Programming Languages and Analysis for Security (PLAS), 2013.
[35]
A. Schröpfer, and F. Kerschbaum. Forecasting Run-Times of Secure Two-Party Computation. In Proceedings of the 8th International Conference on Quantitative Evaluation of Systems (QEST), 2011.
[36]
A. Schröpfer, F. Kerschbaum, and G. Müller. L1 - An Intermediate Language for Mixed-Protocol Secure Computation. In Proceedings of the IEEE Computer Software and Applications Conference (COMPSAC), 2011.
[37]
A. Yao. Protocols for Secure Computations. In Proceedings of the IEEE Symposium on Foundations of Computer Science (FOCS), 1982.
[38]
M. Zohner, and T. Schneider. GMW vs. Yao -- Efficient Secure Two-Party Computation with Low Depth Circuits. In Proceedings of the 17th International Conference on Financial Cryptography and Data Security (FC), 2013.
Index Terms
- Efficient secure computation optimization
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Published In
November 2013
36 pages
ISBN:9781450324892
DOI:10.1145/2517872
- Program Chairs:
- Martin Franz,
- Andreas Holzer,
- Rupak Majumdar,
- Bryan Parno,
- Helmut Veith
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Publication History
Published: 04 November 2013
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CCS'13: 2013 ACM SIGSAC Conference on Computer and Communications Security
November 4, 2013
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PETShop '13 Paper Acceptance Rate 7 of 8 submissions, 88%;
Overall Acceptance Rate 7 of 8 submissions, 88%
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