Advertisement

Fast Large-Scale Honest-Majority MPC for Malicious Adversaries

  • Koji Chida
  • Daniel Genkin
  • Koki Hamada
  • Dai Ikarashi
  • Ryo Kikuchi
  • Yehuda Lindell
  • Ariel Nof
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10993)

Abstract

Protocols for secure multiparty computation enable a set of parties to compute a function of their inputs without revealing anything but the output. The security properties of the protocol must be preserved in the presence of adversarial behavior. The two classic adversary models considered are semi-honest (where the adversary follows the protocol specification but tries to learn more than allowed by examining the protocol transcript) and malicious (where the adversary may follow any arbitrary attack strategy). Protocols for semi-honest adversaries are often far more efficient, but in many cases the security guarantees are not strong enough.

In this paper, we present new protocols for securely computing any functionality represented by an arithmetic circuit. We utilize a new method for verifying that the adversary does not cheat, that yields a cost of just twice that of semi-honest protocols in some settings. Our protocols are information-theoretically secure in the presence of a malicious adversaries, assuming an honest majority. We present protocol variants for small and large fields, and show how to efficiently instantiate them based on replicated secret sharing and Shamir sharing. As with previous works in this area aiming to achieve high efficiency, our protocol is secure with abort and does not achieve fairness, meaning that the adversary may receive output while the honest parties do not.

We implemented our protocol and ran experiments for different numbers of parties, different network configurations and different circuit depths. Our protocol significantly outperforms the previous best for this setting (Lindell and Nof, CCS 2017); for a large number of parties, our implementation runs almost an order of magnitude faster than theirs.

References

  1. 1.
    Araki, T., Barak, A., Furukawa, J., Lichter, T., Lindell, Y., Nof, A., Ohara, K., Watzman, A., Weinstein, O.: Optimized honest-majority MPC for malicious adversaries - breaking the 1 billion-gate per second barrier. In: The IEEE S&P (2017)Google Scholar
  2. 2.
    Araki, T., Furukawa, J., Lindell, Y., Nof, A., Ohara, K.: High-throughput semi-honest secure three-party computation with an honest majority. In: The 23rd ACM CCS, pp. 805–817 (2016)Google Scholar
  3. 3.
    Beaver, D.: Foundations of secure interactive computing. In: Feigenbaum, J. (ed.) CRYPTO 1991. LNCS, vol. 576, pp. 377–391. Springer, Heidelberg (1992).  https://doi.org/10.1007/3-540-46766-1_31CrossRefGoogle Scholar
  4. 4.
    Beerliová-Trubíniová, Z., Hirt, M.: Perfectly-secure MPC with linear communication complexity. In: Canetti, R. (ed.) TCC 2008. LNCS, vol. 4948, pp. 213–230. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-78524-8_13CrossRefGoogle Scholar
  5. 5.
    Ben-Or, M., Goldwasser, S., Wigderson, A.: Completeness theorems for non-cryptographic fault-tolerant distributed computation. In: 20th STOC (1988)Google Scholar
  6. 6.
    Burra, S.S., Larraia, E., Nielsen, J.B., Nordholt, P.S., Orlandi, C., Orsini, E., Scholl, P., Smart, N.P.: High performance multi-party computation for binary circuits based on oblivious transfer. ePrint Cryptology Archive, 2015/472 (2015)Google Scholar
  7. 7.
    Canetti, R.: Security and composition of multiparty cryptographic protocols. J. Cryptol. 13(1), 143–202 (2000)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Chaum, D., Crépeau, C., Damgård, I.: Multi-party unconditionally secure protocols. In: 20th STOC, pp. 11–19 (1988)Google Scholar
  9. 9.
    Cleve, R.: Limits on the security of coin flips when half the processors are faulty. In: 18th STOC, pp. 364–369 (1986)Google Scholar
  10. 10.
    Cramer, R., Damgård, I., Ishai, Y.: Share conversion, pseudorandom secret-sharing and applications to secure computation. In: Kilian, J. (ed.) TCC 2005. LNCS, vol. 3378, pp. 342–362. Springer, Heidelberg (2005).  https://doi.org/10.1007/978-3-540-30576-7_19CrossRefGoogle Scholar
  11. 11.
    Damgård, I., Keller, M., Larraia, E., Pastro, V., Scholl, P., Smart, N.P.: Practical covertly secure MPC for dishonest majority – or: breaking the SPDZ limits. In: Crampton, J., Jajodia, S., Mayes, K. (eds.) ESORICS 2013. LNCS, vol. 8134, pp. 1–18. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40203-6_1CrossRefGoogle Scholar
  12. 12.
    Damgård, I., Nielsen, J.B.: Scalable and unconditionally secure multiparty computation. In: Menezes, A. (ed.) CRYPTO 2007. LNCS, vol. 4622, pp. 572–590. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-74143-5_32CrossRefGoogle Scholar
  13. 13.
    Damgård, I., Pastro, V., Smart, N.P., 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).  https://doi.org/10.1007/978-3-642-32009-5_38CrossRefGoogle Scholar
  14. 14.
    Genkin, D., Ishai, Y., Prabhakaran, M., Sahai, A., Tromer, E.: Circuits resilient to additive attacks with applications to secure computation. In: STOC 2014 (2014)Google Scholar
  15. 15.
    Genkin, D., Ishai, Y., Polychroniadou, A.: Efficient multi-party computation: from passive to active security via secure SIMD circuits. In: Gennaro, R., Robshaw, M. (eds.) CRYPTO 2015. LNCS, vol. 9216, pp. 721–741. Springer, Heidelberg (2015).  https://doi.org/10.1007/978-3-662-48000-7_35CrossRefGoogle Scholar
  16. 16.
    Goldreich, O., Micali, S., Wigderson, A.: How to play any mental game. In: 19th STOC, pp. 218–229 (1987)Google Scholar
  17. 17.
    Goldwasser, S., Levin, L.: Fair computation of general functions in presence of immoral majority. In: Menezes, A.J., Vanstone, S.A. (eds.) CRYPTO 1990. LNCS, vol. 537, pp. 77–93. Springer, Heidelberg (1991).  https://doi.org/10.1007/3-540-38424-3_6CrossRefGoogle Scholar
  18. 18.
    Gennaro, R., Rabin, M., Rabin, T.: Simplified VSS and fact-track multiparty computations with applications to threshold cryptography. In: 17th PODC (1998)Google Scholar
  19. 19.
    Goldreich, O.: Foundations of Cryptography: Basic Applications, vol. 2 (2004)Google Scholar
  20. 20.
    Keller, M., Orsini, E., Scholl, P.: MASCOT: faster malicious arithmetic secure computation with oblivious transfer. In: 23rd ACM CCS, pp. 830–842 (2016)Google Scholar
  21. 21.
    Keller, M., Pastro, V., Rotaru, D.: Overdrive: making SPDZ great again. In: Nielsen, J.B., Rijmen, V. (eds.) EUROCRYPT 2018. LNCS, vol. 10822, pp. 158–189. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-78372-7_6CrossRefGoogle Scholar
  22. 22.
    Lindell, Y., Nof, A.: A framework for constructing fast MPC over arithmetic circuits with malicious adversaries and an honest-majority. In: ACM CCS (2017)Google Scholar
  23. 23.
    Mohassel, P., Rosulek, M., Zhang, Y.: Fast and secure three-party computation: the garbled circuit approach. In: ACM CCS, pp. 591–602 (2015)Google Scholar
  24. 24.
    Rabin, T., Ben-Or, M.: Verifiable secret sharing and multi-party protocols with honest majority. In: 21st STOC, pp. 73–85 (1989)Google Scholar
  25. 25.
    Shamir, A.: How to share a secret. CACM 22(11), 612–613 (1979)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Yao, A.: How to generate and exchange secrets. In: 27th FOCS, pp. 162–167 (1986)Google Scholar

Copyright information

© International Association for Cryptologic Research 2018

Authors and Affiliations

  • Koji Chida
    • 1
  • Daniel Genkin
    • 2
  • Koki Hamada
    • 1
  • Dai Ikarashi
    • 1
  • Ryo Kikuchi
    • 1
  • Yehuda Lindell
    • 3
  • Ariel Nof
    • 3
  1. 1.NTT Secure Platform LaboratoriesTokyoJapan
  2. 2.University of MichiganAnn ArborUSA
  3. 3.Bar-Ilan UniversityRamat GanIsrael

Personalised recommendations