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Privacy-Preserving Multi-Objective Evolutionary Algorithms

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

Abstract

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.

Keywords

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

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

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