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Integrating domain and constraint privacy reasoning in the distributed stochastic algorithm with breakouts

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Abstract

Privacy has traditionally been a major motivation of distributed problem solving. One popular approach to enable privacy in distributed environments is to implement complex cryptographic protocols. In this paper, we propose a different, orthogonal approach, which is to control the quality and the quantity of publicized data. We consider the Open Constraint Programming model and focus on algorithms that solve Distributed Constraint Optimization Problems (DCOPs) using a local search approach. Two such popular algorithms exist to find good solutions to DCOP: DSA and GDBA. In this paper, we propose DSAB, a new algorithm that merges ideas from both algorithms to allow extensive handling of constraint privacy. We also study how algorithms behave when solving Utilitarian DCOPs, where utilitarian agents want to reach an agreement while reducing the privacy loss. We experimentally study how the utilitarian approach impacts the quality of the solution and of publicized data.

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Authors and Affiliations

  1. Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, F-59313, Valenciennes, France

    Julien Vion, René Mandiau & Sylvain Piechowiak

  2. Florida Institute of Technology, Melbourne, FL, USA

    Marius Silaghi

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  1. Julien Vion
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  2. René Mandiau
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  3. Sylvain Piechowiak
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  4. Marius Silaghi
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Correspondence to Julien Vion.

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This paper is an extension of our work published to ISAIM’2020 [37].

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Vion, J., Mandiau, R., Piechowiak, S. et al. Integrating domain and constraint privacy reasoning in the distributed stochastic algorithm with breakouts. Ann Math Artif Intell 90, 31–73 (2022). https://doi.org/10.1007/s10472-021-09735-5

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