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
-
CC BY
BibTeX
@misc{cryptoeprint:2010/326,
author = {Daniel Funke and Florian Kerschbaum},
title = {Privacy-Preserving Multi-Objective Evolutionary Algorithms},
howpublished = {Cryptology {ePrint} Archive, Paper 2010/326},
year = {2010},
url = {https://eprint.iacr.org/2010/326}
}
