CheapSMC: A Framework to Minimize Secure Multiparty Computation Cost in the Cloud

  • Erman Pattuk
  • Murat Kantarcioglu
  • Huseyin Ulusoy
  • Bradley Malin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9766)

Abstract

Secure multi-party computation (SMC) techniques are increasingly more efficient and practical, due in part, to various improvements. For instance, recent research has shown that different protocols that are implemented using different sharing mechanisms (e.g., boolean and arithmetic sharings) can have varying computational and communication costs. Although there are some approaches to automatically mix protocols of different sharing schemes to enhance execution efficiency, none provide a generic optimization framework to discover the least expensive mixed-protocol SMC execution for cloud deployment.

In this work, we introduce a generic SMC optimization framework CheapSMC that can invoke any mixed-protocol SMC circuit evaluation tool as a black box to uncover the cheapest SMC cloud deployment option. To do so, CheapSMC computes one-time benchmarks for the target cloud service and gathers performance statistics for basic circuit components. Relying on these statistics, an optimization layer of CheapSMC invokes several heuristics to find the cheapest mix-protocol circuit evaluation. Subsequently, the optimized circuit is passed to a mixed-protocol SMC tool for actual executable generation. Our empirical results, gathered by running cases studies on large range of complexity in data volume and functions for computation, show that significant cost savings can be achieved via our optimization framework in comparison to the state-of-the-art.

References

  1. 1.
    Sadeghi, A.-R., Schneider, T., Wehrenberg, I.: Efficient privacy-preserving face recognition. In: Lee, D., Hong, S. (eds.) ICISC 2009. LNCS, vol. 5984, pp. 229–244. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Beaver, D.: Efficient multiparty protocols using circuit randomization. In: Feigenbaum, J. (ed.) Advances in Cryptology – CRYPTO ’91. LNCS, pp. 420–432. Springer, Heidelberg (1992)Google Scholar
  3. 3.
    Evans, D., et al.: Efficient privacy-preserving biometric identification. In: NDSS (2011)Google Scholar
  4. 4.
    Malkhi, D., et al.: Fairplay-secure two-party computation system. In: USENIX Security (2004)Google Scholar
  5. 5.
    Bogdanov, D., Laur, S., Willemson, J.: Sharemind: a framework for fast privacy-preserving computations. In: Jajodia, S., Lopez, J. (eds.) ESORICS 2008. LNCS, vol. 5283, pp. 192–206. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Demmler, D., et al.: ABY - a framework for efficient mixed-protocol secure two-party computation. In: NDSS (2015)Google Scholar
  7. 7.
    Kerschbaum, F., Schneider, T., Schröpfer, A.: Automatic protocol selection in secure two-party computations. In: Boureanu, I., Owesarski, P., Vaudenay, S. (eds.) ACNS 2014. LNCS, vol. 8479, pp. 566–584. Springer, Heidelberg (2014)Google Scholar
  8. 8.
    Forbes. Cloud computing: United states businesses will spend $13 billion on it (2014). http://www.forbes.com/sites/tjmccue/2014/01/29/cloud-computing-united-states-businesses-will-spend-13-billion-on-it
  9. 9.
    Barni, M., et al.: Privacy-preserving fingercode authentication. In: ACM workshop on Multimedia and security, pp. 231–240. ACM (2010)Google Scholar
  10. 10.
    Naor, M., et al.: Efficient oblivious transfer protocols. In: SIAM, pp. 448–457 (2001)Google Scholar
  11. 11.
    Rabin, M.O.: How to exchange secrets with oblivious transfer. IACR Cryptology ePrint Arch. 2005, 187 (2005)Google Scholar
  12. 12.
    Henecka, W., et al.: Tasty: tool for automating secure two-party computations. In: ACM CCS, pp. 451–462 (2010)Google Scholar
  13. 13.
    Yao, A.C.: Protocols for secure computations. In: IEEE ASFCS, pp. 160–164. IEEE (1982)Google Scholar
  14. 14.
    Erkin, Z., Franz, M., Guajardo, J., Katzenbeisser, S., Lagendijk, I., Toft, T.: Privacy-preserving face recognition. In: Goldberg, I., Atallah, M.J. (eds.) PETS 2009. LNCS, vol. 5672, pp. 235–253. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  • Erman Pattuk
    • 1
  • Murat Kantarcioglu
    • 1
  • Huseyin Ulusoy
    • 1
  • Bradley Malin
    • 2
  1. 1.The University of Texas at DallasRichardsonUSA
  2. 2.Vanderbilt UniversityNashvilleUSA

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