Privacy-Preserving Multi-Objective Evolutionary Algorithms

  • Daniel Funke
  • Florian Kerschbaum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6239)


Existing privacy-preserving evolutionary algorithms are limited to specific problems securing only cost function evaluation. This lack of functionality and security prevents their use for many security sensitive business optimization problems, such as our use case in collaborative supply chain management. We present a technique to construct privacy-preserving algorithms that address multi-objective problems and secure the entire algorithm including survivor selection. We improve performance over Yao’s protocol for privacy-preserving algorithms and achieve solution quality only slightly inferior to the multi-objective evolutionary algorithm NSGA-II.


Secret Sharing Communication Complexity Secret Share Scheme Homomorphic Encryption Nondominated Sorting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Han, S., Ng, W.: Privacy-preserving genetic algorithms for rule discovery. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, vol. 4654, pp. 407–417. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Sakuma, J., Kobayashi, S.: A genetic algorithm for privacy preserving combinatorial optimization. In: Proc. GECCO 2007, pp. 1372–1379. ACM, New York (2007)CrossRefGoogle Scholar
  3. 3.
    Yao, A.C.: Protocols for secure computations. In: Proc. IEEE FOCS 1982, Washington, DC, USA, pp. 160–164. IEEE Computer Society, Los Alamitos (1982)Google Scholar
  4. 4.
    Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, Springer, Heidelberg (2000)Google Scholar
  5. 5.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar
  6. 6.
    Diponegoro, A., Sarker, B.: Finite horizon planning for a production system with permitted shortage and fixed-interval deliveries. Computers and Operations Research 33(8), 2387–2404 (2006)zbMATHCrossRefGoogle Scholar
  7. 7.
    Funke, D., Kerschbaum, F.: Privacy-preserving multi-objective evolutionary algorithms. Cryptology ePrint Archive, Report 2010/326 (2010)Google Scholar
  8. 8.
    Malkhi, D., Nisan, N., Pinkas, B., Sella, Y.: Fairplay - a secure two-party computation system. In: Proc. USENIX Security 2004, pp. 287–302. USENIX Association, Berkeley (2004)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Karnin, E., Greene, J., Hellman, M.: On secret sharing systems. IEEE Transactions on Information Theory 29(1), 35–41 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)CrossRefGoogle Scholar
  12. 12.
    Coello Coello, C., Lamont, G., van Veldhuizen, D.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  13. 13.
    Batcher, K.E.: Sorting networks and their applications. In: Proceedings of the Spring Joint Computer Conference, April 30-May 2, pp. 307–314. ACM, New York (1968)Google Scholar
  14. 14.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Technical Report TIK-Report 103, ETH Zurich, Zurich, Switzerland (2001)Google Scholar
  15. 15.
    Knowles, J., Corne, D.: Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation 8(2), 149–172 (2000)CrossRefGoogle Scholar
  16. 16.
    Kerschbaum, F.: Practical privacy-preserving benchmarking. In: Proc. IFIP SEC 2008, Boston, MA, USA, pp. 17–31. Springer, Heidelberg (2008)Google Scholar
  17. 17.
    Kerschbaum, F., Dahlmeier, D., Schröpfer, A., Biswas, D.: On the practical importance of communication complexity for secure multi-party computation protocols. In: Proc. ACM SAC 2009, pp. 2008–2015. ACM, New York (2009)CrossRefGoogle Scholar
  18. 18.
    Kerschbaum, F., Oertel, N., Weiss Ferreira Chaves, L.: Privacy-preserving computation of benchmarks on item-level data using RFID. In: Proc. ACM WiSec 2010, pp. 105–110. ACM, New York (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Daniel Funke
    • 1
  • Florian Kerschbaum
    • 1
  1. 1.SAP Research CEC KarlsruheKarlsruhe

Personalised recommendations