International Conference on Financial Cryptography and Data Security

FC 2015: Financial Cryptography and Data Security pp 172-183 | Cite as

Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point Computations

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8976)

Abstract

Two of the major branches in secure multi-party computation research are secret sharing and garbled circuits. This work succeeds in combining these to enable seamlessly switching to the technique more efficient for the required functionality. As an example, we add garbled circuits based IEEE 754 floating-point numbers to a secret sharing environment achieving very high efficiency and the first, to our knowledge, fully IEEE 754 compliant secure floating-point implementation.

References

  1. 1.
    Aliasgari, M., Blanton, M., Zhang, Y., Steele, A.: Secure computation on floating point numbers. In: Proceedings of NDSS 2013. The Internet Society (2013)Google Scholar
  2. 2.
    Bellare, M., Hoang, V.T., Keelveedhi, S., Rogaway, P.: Efficient garbling from a fixed-key blockcipher. In: Proceedings of SP 2013, pp. 478–492. IEEE Computer Society, Washington, DC (2013)Google Scholar
  3. 3.
    Bellare, M., Hoang, V.T., Rogaway, P.: Foundations of garbled circuits. In: Proceedings of CCS 2012, pp. 784–796. ACM, New York (2012)Google Scholar
  4. 4.
    Ben-David, A., Nisan, N., Pinkas, B.: FairplayMP: a system for secure multi-party computation. In: Proceedings of CCS 2008, pp. 257–266. ACM (2008)Google Scholar
  5. 5.
    Bogdanov, D.: Sharemind: programmable secure computations with practical applications. Ph.D. thesis. University of Tartu (2013)Google Scholar
  6. 6.
    Bogdanov, D., Laud, P., Laur, S., Pullonen, P.: From input private to universally composable secure multi-party computation. In: Proceedings of CSF 2014. IEEE Computer Society (2014)Google Scholar
  7. 7.
    Bogdanov, D., Laud, P., Randmets, J.: Domain-polymorphic programming of privacy-preserving applications. In: Proceedings of PETShop 2013, pp. 23–26. ACM (2013)Google Scholar
  8. 8.
    Bogdanov, D., Niitsoo, M., Toft, T., Willemson, J.: High-performance secure multi-party computation for data mining applications. IJIS 11(6), 403–418 (2012)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Damgård, I., Pastro, V., Smart, N., Zakarias, S.: Multiparty computation from somewhat homomorphic encryption. In: Safavi-Naini, R., Canetti, R. (eds.) CRYPTO 2012. LNCS, vol. 7417, pp. 643–662. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  11. 11.
    Franz, M., Katzenbeisser, S.: Processing encrypted floating point signals. In: Proceedings of MM&Sec 2011, pp. 103–108. ACM, New York (2011)Google Scholar
  12. 12.
    Goldberg, D.: What every computer scientist should know about floating-point arithmetic. ACM Comput. Surv. 23(1), 5–48 (1991)CrossRefGoogle Scholar
  13. 13.
    Henecka, W., Kögl, S., Sadeghi, A.R., Schneider, T., Wehrenberg, I.: TASTY: tool for automating secure two-party computations. In: Proceedings of CCS 2010, pp. 451–462. ACM, New York (2010)Google Scholar
  14. 14.
    Holzer, A., Franz, M., Katzenbeisser, S., Veith, H.: Secure two-party computations in ANSI C. In: Proceedings of CCS 2012, pp. 772–783. ACM (2012)Google Scholar
  15. 15.
    Huang, Y., Evans, D., Katz, J., Malka, L.: Faster secure two-party computation using garbled circuits. In: Proceedings of SEC 2011. USENIX Association (2011)Google Scholar
  16. 16.
    754-2008 - IEEE standard for floating-point arithmetic (2008). http://ieeexplore.ieee.org/servlet/opac?punumber=4610933
  17. 17.
    Kamm, L., Willemson, J.: Secure floating-point arithmetic and private satellite collision analysis. IJIS (2014). http://link.springer.com/article/10.1007%2Fs10207-014-0271-8
  18. 18.
    Kolesnikov, V., Schneider, T.: Improved garbled circuit: free XOR gates and applications. In: Aceto, L., Damgård, I., Goldberg, L.A., Halldórsson, M.M., Ingólfsdóttir, A., Walukiewicz, I. (eds.) ICALP 2008, Part II. LNCS, vol. 5126, pp. 486–498. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  19. 19.
    Kreuter, B., Mood, B., Shelat, A., Butler, K.: PCF: a portable circuit format for scalable two-party secure computation. In: Proceedings of SEC 2013, pp. 321–336. USENIX Association, Berkeley (2013)Google Scholar
  20. 20.
    Kreuter, B., Shelat, A., Shen, C.: Billion-gate secure computation with malicious adversaries. In: Proceedings of Security 2012. USENIX Association (2012)Google Scholar
  21. 21.
    Lindell, Y., Pinkas, B.: A proof of security of Yao’s protocol for two-party computation. J. Cryptol. 22(2), 161–188 (2009)MATHMathSciNetCrossRefGoogle Scholar
  22. 22.
    Liu, Y.C., Chiang, Y.T., Hsu, T.S., Liau, C.J., Wang, D.W.: Floating point arithmetic protocols for constructing secure data analysis application. Procedia Comput. Sci. 22, 152–161 (2013)CrossRefGoogle Scholar
  23. 23.
  24. 24.
    Pinkas, B., Schneider, T., Smart, N.P., Williams, S.C.: Secure two-party computation is practical. In: Matsui, M. (ed.) ASIACRYPT 2009. LNCS, vol. 5912, pp. 250–267. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  25. 25.
    Pullonen, P., Siim, S.: Combining secret sharing and garbled circuits for efficient private IEEE 754 floating-point computations. Cryptology ePrint Archive, Report 2014/990 (2014)Google Scholar
  26. 26.
    Seroussi, G.: Table of low-weight binary irreducible polynomials (1998). http://www.hpl.hp.com/techreports/98/HPL-98-135.html
  27. 27.
    Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)MATHMathSciNetCrossRefGoogle Scholar
  28. 28.
  29. 29.
    Yao, A.C.: Protocols for secure computations. In: Proceedings of SFCS 1982, pp. 160–164. IEEE Computer Society, Washington, DC (1982)Google Scholar

Copyright information

© International Financial Cryptography Association 2015

Authors and Affiliations

  1. 1.Cybernetica ASTartuEstonia
  2. 2.University of TartuTartuEstonia

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