Automatic Protocol Selection in Secure Two-Party Computations

  • Florian Kerschbaum
  • Thomas Schneider
  • Axel Schröpfer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8479)

Abstract

Performance of secure computation is still often an obstacle to its practical adaption. There are different protocols for secure computation that compete for the best performance. In this paper we propose automatic protocol selection which selects a protocol for each operation resulting in a mix with the best performance so far. Based on an elaborate performance model, we propose an optimization algorithm and an efficient heuristic for this selection problem. We show that our mixed protocols achieve the best performance on a set of use cases. Furthermore, our results underpin that the selection problem is so complicated and large in size, that a programmer is unlikely to manually make the optimal selection. Our proposed algorithms nevertheless can be integrated into a compiler in order to yield the best (or near-optimal) performance.

Keywords

Secure Two-Party Computation Performance Optimization Protocol Selection 

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References

  1. 1.
    Atallah, M., Bykova, M., Li, J., Frikken, K., Topkara, M.: Private Collaborative Forecasting and Benchmarking. In: ACM Privacy in the Electronic Society, WPES (2004)Google Scholar
  2. 2.
    Banerjee, A.: A Joint Economic-Lot-Size Model For Purchaser and Vendor. Decision Sciences 17(3) (1986)Google Scholar
  3. 3.
    Blanton, M., Gasti, P.: Secure and Efficient Protocols for Iris and Fingerprint Identification. In: Atluri, V., Diaz, C. (eds.) ESORICS 2011. LNCS, vol. 6879, pp. 190–209. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Bogdanov, D., Talviste, R., Willemson, J.: Deploying Secure Multi-Party Computation for Financial Data Analysis. In: Keromytis, A.D. (ed.) FC 2012. LNCS, vol. 7397, pp. 57–64. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Bogetoft, P., Christensen, D.L., Damgård, I., Geisler, M., Jakobsen, T., Krøigaard, M., Nielsen, J.D., Nielsen, J.B., Nielsen, K., Pagter, J., Schwartzbach, M., Toft, T.: Secure Multiparty Computation Goes Live. In: Dingledine, R., Golle, P. (eds.) FC 2009. LNCS, vol. 5628, pp. 325–343. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Bunn, P., Ostrovsky, R.: Secure Two-Party k-Means Clustering. ACM Computer and Communications Security, CCS (2007)Google Scholar
  8. 8.
    Choi, S.G., Hwang, K.-W., Katz, J., Malkin, T., Rubenstein, D.: Secure Multi-Party Computation of Boolean Circuits with Applications to Privacy in On-Line Marketplaces. In: Dunkelman, O. (ed.) CT-RSA 2012. LNCS, vol. 7178, pp. 416–432. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Damgård, I., Geisler, M., Krøigaard, M., Nielsen, J.B.: Asynchronous Multiparty Computation: Theory and Implementation. In: Jarecki, S., Tsudik, G. (eds.) PKC 2009. LNCS, vol. 5443, pp. 160–179. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Damgård, I., Thorbek, R.: Efficient Conversion of Secret-Shared Values Between Different Fields, http://eprint.iacr.org/2008/221
  11. 11.
    De Cristofaro, E., Jarecki, S., Kim, J., Tsudik, G.: Privacy-Preserving Policy-Based Information Transfer. In: Goldberg, I., Atallah, M.J. (eds.) PETS 2009. LNCS, vol. 5672, pp. 164–184. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Gentry, C.: Fully Homomorphic Encryption using Ideal Lattices. In: ACM Symposium on Theory of Computing, STOC (2009)Google Scholar
  13. 13.
    Gentry, C., Halevi, S.: Implementing Gentry’s Fully-Homomorphic Encryption Scheme. In: Paterson, K.G. (ed.) EUROCRYPT 2011. LNCS, vol. 6632, pp. 129–148. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Goethals, B., Laur, S., Lipmaa, H., Mielikäinen, T.: On Private Scalar Product Computation for Privacy-Preserving Data Mining. In: Park, C.-S., Chee, S. (eds.) ICISC 2004. LNCS, vol. 3506, pp. 104–120. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Goldreich, O.: Foundations of Cryptography: Volume 2 – Basic Applications. Cambridge Univ. Press (2004)Google Scholar
  16. 16.
    Goldreich, O., Micali, S., Wigderson, A.: How to Play Any Mental Game. In: ACM Symposium on Theory of Computing, STOC (1987)Google Scholar
  17. 17.
    Henecka, W., Kögl, S., Sadeghi, A.-R., Schneider, T., Wehrenberg, I.: TASTY: Tool for Automating Secure Two-partY computations. In: ACM Computer and Communications Security, CCS (2010)Google Scholar
  18. 18.
    Holzer, A., Franz, M., Katzenbeisser, S., Veith, H.: Secure Two-Party Computation in ANSI C. In: ACM Computer and Communications Security, CCS (2012)Google Scholar
  19. 19.
    Huang, Y., Evans, D., Katz, J.: Private Set Intersection: Are Garbled Circuits Better than Custom Protocols? In: Network and Distributed System Security, NDSS (2012)Google Scholar
  20. 20.
    Huang, Y., Evans, D., Katz, J., Malka, L.: Faster Secure Two-Party Computation Using Garbled Circuits. In: USENIX Security Symposium (2011)Google Scholar
  21. 21.
    Huang, Y., Malka, L., Evans, D., Katz, J.: Efficient Privacy-Preserving Biometric Identification. In: Network and Distributed System Security, NDSS (2011)Google Scholar
  22. 22.
    Ishai, Y., Kilian, J., Nissim, K., Petrank, E.: Extending Oblivious Transfers Efficiently. In: Boneh, D. (ed.) CRYPTO 2003. LNCS, vol. 2729, pp. 145–161. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  23. 23.
    Kerschbaum, F.: Practical Privacy-Preserving Benchmarking. In: IFIP International Information Security Conference, SEC (2008)Google Scholar
  24. 24.
    Kerschbaum, F.: Automatically Optimizing Secure Computation. In: ACM Computer and Communications Security, CCS (2011)Google Scholar
  25. 25.
    Kerschbaum, F., Schröpfer, A., Zilli, A., Pibernik, R., Catrina, O., de Hoogh, S., Schoenmakers, B., Cimato, S., Damiani, E.: Secure Collaborative Supply Chain Management. IEEE Computer 44(9) (2011)Google Scholar
  26. 26.
    Kerschbaum, F., Schneider, T., Schröpfer, A.: Automatic Protocol Selection in Secure Two-Party Computations (Full Version), http://eprint.iacr.org/2014/200
  27. 27.
    Kolesnikov, V., Sadeghi, A.-R., Schneider, T.: Improved Garbled Circuit Building Blocks and Applications to Auctions and Computing Minima. In: Garay, J.A., Miyaji, A., Otsuka, A. (eds.) CANS 2009. LNCS, vol. 5888, pp. 1–20. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  28. 28.
    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
  29. 29.
    Lindell, Y., Pinkas, B.: Privacy Preserving Data Mining. Journal of Cryptology 15(3) (2002)Google Scholar
  30. 30.
    Lindell, Y., Pinkas, B.: A Proof of Yao’s Protocol for Secure Two-Party Computation. Journal of Cryptology 22(2) (2009)Google Scholar
  31. 31.
    MacKenzie, P.D., Oprea, A., Reiter, M.K.: Automatic Generation of Two-Party Computations. ACM Computer and Communications Security, CCS (2003)Google Scholar
  32. 32.
    Malka, L.: VMCrypt - Modular Software Architecture for Scalable Secure Computation. ACM Computer and Communications Security, CCS (2011)Google Scholar
  33. 33.
    Malkhi, D., Nisan, N., Pinkas, B., Sella, Y.: Fairplay - A Secure Two-party Computation System. In: USENIX Security Symposium (2004)Google Scholar
  34. 34.
    Mitchell, T.M.: Machine Learning. McGraw-Hill (1997)Google Scholar
  35. 35.
    Mood, B., Letaw, L., Butler, K.: Memory-Efficient Garbled Circuit Generation for Mobile Devices. In: Keromytis, A.D. (ed.) FC 2012. LNCS, vol. 7397, pp. 254–268. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  36. 36.
    Naor, M., Pinkas, B.: Efficient Oblivious Transfer Protocols. In: Symposium on Data Structures and Algorithms, SODA (2001)Google Scholar
  37. 37.
    Naor, M., Pinkas, B., Sumner, R.: Privacy Preserving Auctions and Mechanism Design. In: ACM Conference on Electronic Commerce (EC) (1999)Google Scholar
  38. 38.
    NIST. Recommendation for Key Management. Special Publication 800-57 Part 1 Rev. 3, 07/2012Google Scholar
  39. 39.
    Paillier, P.: Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  40. 40.
    Pibernik, R., Zhang, Y., Kerschbaum, F., Schröpfer, A.: Secure Collaborative Supply Chain Planning and Inverse Optimization - The JELS Model. European Journal of Operational Research (EJOR) 208(1) (2011)Google Scholar
  41. 41.
    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
  42. 42.
    Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1) (1986)Google Scholar
  43. 43.
    Schröpfer, A., Kerschbaum, F.: Forecasting Run-Times of Secure Two-Party Computation. In: Int. Conference on Quantitative Evaluation of Systems, QEST (2011)Google Scholar
  44. 44.
    Schröpfer, A., Kerschbaum, F., Müller, G.: L1 - An Intermediate Language for Mixed-Protocol Secure Computation. In: IEEE Computer Software and Applications Conference, COMPSAC (2011)Google Scholar
  45. 45.
    Yao, A.C.: How to Generate and Exchange Secrets. In: IEEE Foundations of Computer Science, FOCS (1986)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Florian Kerschbaum
    • 1
  • Thomas Schneider
    • 2
  • Axel Schröpfer
    • 1
  1. 1.SAPKarlsruheGermany
  2. 2.Technische Universität DarmstadtGermany

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