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Paper 2010/326

Privacy-Preserving Multi-Objective Evolutionary Algorithms

Daniel Funke and Florian Kerschbaum

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.

Metadata
Available format(s)
PDF
Category
Applications
Publication info
Published elsewhere. A short version of the paper has been accepted for publication at PPSN2010
Keywords
secure computationevolutionary algorithms
Contact author(s)
daniel funke @ ieee org
History
2010-09-13: revised
2010-06-04: received
See all versions
Short URL
https://ia.cr/2010/326
License
Creative Commons Attribution
CC BY
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