Heterogeneous Secure Multi-Party Computation

  • Mentari Djatmiko
  • Mathieu Cunche
  • Roksana Boreli
  • Aruna Seneviratne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7290)


The increased processing power and storage capacity of inhome and mobile computing devices has motivated their inclusion in distributed and cloud computing systems. The resulting diverse environment creates a strong requirement for secure computations, which can be realised by Secure Multi-Party Computation (MPC). However, MPC most commonly assumes that parties performing the secure computation have the same characteristics and evenly distributes the computation load. In a heterogeneous environment, MPC using the same approach would result in poor performance. In this paper, we propose a mechanism for MPC share distribution in such an environment and present an analysis of the gain in robustness and the corresponding computational and communication complexity. Our results show that the uneven share distribution is a worthwhile approach in diverse computing systems.


secure multi-party computation distributed computing unequal shares heterogeneous platforms 


  1. 1.
    Alba, E., Cotta, C.: Evolutionary algorithms. In: Handbook of Bioinspired Algorithms and Applications, ch. 2, pp. 3–19. Chapman & Hall (2006)Google Scholar
  2. 2.
    Ben-Or, M., Goldwasser, S., Wigderson, A.: Completeness theorems for non-cryptographic fault-tolerant distributed computation. In: Proceedings of the 20th Annual ACM Symposium on Theory of Computing, pp. 1–10. ACM, New York (1988)Google Scholar
  3. 3.
  4. 4.
    Brickell, E.: Some Ideal Secret Sharing Schemes. In: Quisquater, J.-J., Vandewalle, J. (eds.) EUROCRYPT 1989. LNCS, vol. 434, pp. 468–475. Springer, Heidelberg (1990)Google Scholar
  5. 5.
    Burkhart, M., Strasser, M., Many, D., Dimitropoulos, X.: SEPIA: privacy-preserving aggregation of multi-domain network events and statistics. In: Proceedings of the 19th USENIX Conference on Security, p. 15. USENIX Association, Berkeley (2010)Google Scholar
  6. 6.
    Cao, N., Yang, Z., Wang, C., Ren, K., Lou, W.: Privacy-preserving query over encrypted graph-structured data in cloud computing. In: 2011 31st International Conference on ICDCS, pp. 393–402 (June 2011)Google Scholar
  7. 7.
    Cramer, R., Damgaard, I., Nielsen, J.B.: Multiparty Computation, an Introduction (May 2008)Google Scholar
  8. 8.
    Cramer, R., Damgård, I., Maurer, U.: General Secure Multi-party Computation from any Linear Secret-Sharing Scheme. In: Preneel, B. (ed.) EUROCRYPT 2000. LNCS, vol. 1807, pp. 316–334. Springer, Heidelberg (2000)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., Desmedt, Y., Fitzi, M., Nielsen, J.B.: Secure Protocols with Asymmetric Trust. In: Kurosawa, K. (ed.) ASIACRYPT 2007. LNCS, vol. 4833, pp. 357–375. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Dongara, J.: Linpack for android,
  12. 12.
    Estrin, D.: Participatory sensing: applications and architecture [internet predictions]. IEEE Internet Computing 14(1), 12–42 (2010)CrossRefGoogle Scholar
  13. 13.
    Goldman, R.: Pyramid Algorithms: A Dynamic Programming Approach to Curves and Surfaces for Geometric Modeling. In: Lagrange Interpolation and Neville’s Algorithm, ch. 2. Morgan Kaufmann (2003)Google Scholar
  14. 14.
    Kannan, S., Gavrilovska, A., Schwan, K.: Cloud4home – enhancing data services with @home clouds. In: 2011 31st International Conference on Distributed Computing Systems (ICDCS), pp. 539–548 (June 2011)Google Scholar
  15. 15.
    Michiardi, P., Molva, R.: Core: a collaborative reputation mechanism to enforce node cooperation in mobile ad hoc networks. In: Conference on Communications and Multimedia Security, p. 121. Kluwer, BV (2002)Google Scholar
  16. 16.
    Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Verizon: Ip latency statistics (2011),
  18. 18.
    Wang, C., Ren, K., Wang, J., Urs, K.: Harnessing the cloud for securely solving large-scale systems of linear equations. In: 2011 31st International Conference on ICDCS, pp. 549–558 (June 2011)Google Scholar
  19. 19.
    Yao, A.C.: Protocols for secure computations. In: Annual IEEE Symposium on Foundations of Computer Science, pp. 160–164 (1982)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Mentari Djatmiko
    • 1
    • 2
  • Mathieu Cunche
    • 1
  • Roksana Boreli
    • 1
    • 2
  • Aruna Seneviratne
    • 1
    • 2
  1. 1.NICTAEveleighAustralia
  2. 2.University of New South WalesSydneyAustralia

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