Utilitarian approach to privacy in distributed constraint optimization problems

Julien Savaux, Julien Vion, Sylvain Piechowiak, Réne Mandiau, Toshihiro Matsui, Katsutoshi Hirayama, Makoto Yokoo, Shakre Elmane, Marius Silaghi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Privacy has been a major motivation for distributed problem optimization. However, even though several methods have been proposed to evaluate it, none of them is widely used. The Distributed Constraint Optimization Problem (DCOP) is a fundamental model used to approach various families of distributed problems. Here we approach the problem by letting both the optimized costs found in DCOPs and the privacy requirements guide the agents' exploration of the search space. We introduce Utilitarian Distributed Constraint Optimization Problem (UDCOP) where the costs and the privacy requirements are used as parameters to a heuristic modifying the search process. Common stochastic algorithms for decentralized constraint optimization problems are evaluated here according to how well they preserve privacy.

Original languageEnglish
Title of host publicationFLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference
EditorsVasile Rus, Zdravko Markov
PublisherAAAI Press
Pages454-459
Number of pages6
ISBN (Electronic)9781577357872
Publication statusPublished - Jan 1 2017
Event30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017 - Marco Island, United States
Duration: May 22 2017May 24 2017

Publication series

NameFLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference

Other

Other30th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2017
CountryUnited States
CityMarco Island
Period5/22/175/24/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software

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