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

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 qu...

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Veröffentlicht in:	Annals of mathematics and artificial intelligence Jg. 90; H. 1; S. 31 - 73
Hauptverfasser:	Vion, Julien, Mandiau, René, Piechowiak, Sylvain, Silaghi, Marius
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.01.2022 Springer Springer Nature B.V Springer Verlag
Schlagworte:	Algorithms Analysis Artificial Intelligence Complex Systems Computer Science Constraint modelling Costs Cryptography Mathematics Privacy Variables Distributed constraint problems Privacy Local search 68T42 Multi-agent system "Privacy" "Multi-agent system" "Distributed constraint problems" "Local search"
ISSN:	1012-2443, 1573-7470
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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.
AbstractList	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. Keywords Multi-agent system * Privacy * Distributed constraint problems * Local search Mathematics Subject Classification (2010) 68T42 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.
Audience	Academic
Author	Vion, Julien Silaghi, Marius Mandiau, René Piechowiak, Sylvain
Author_xml	– sequence: 1 givenname: Julien orcidid: 0000-0003-1539-9846 surname: Vion fullname: Vion, Julien email: julien.vion@uphf.fr organization: Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201 – sequence: 2 givenname: René surname: Mandiau fullname: Mandiau, René organization: Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201 – sequence: 3 givenname: Sylvain surname: Piechowiak fullname: Piechowiak, Sylvain organization: Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201 – sequence: 4 givenname: Marius surname: Silaghi fullname: Silaghi, Marius organization: Florida Institute of Technology
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Keywords	Distributed constraint problems Privacy Local search 68T42 Multi-agent system "Privacy" "Multi-agent system" "Distributed constraint problems" "Local search"
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References_xml	– reference: Hamadi, Y., Bessiere, C., Quinqueton, J.: Backtracking in Distributed Constraint Networks. In: Proc. of 13Th European Conf. on Artificial Intelligence (ECAI). Brighton, UK, pp 219–223 (1998) – reference: Hewitt, C., Bishop, P., Steiger, R.: A universal modular ACTOR formalism for artificial intelligence (1973) – reference: Zivan, R., Okamoto, S., Peled, H.: Explorative anytime local search for distributed constraint optimization. In: Artificial Intelligence, vol. 212, pp 1–26 (2014) – reference: Yokoo, M., Durfee, E.H., Ishida, T., Kuwabara, K.: The distributed constraint satisfaction problem: formalization and algorithms. In: IEEE Transactions on Knowledge and Data Engineering, vol. 10.5, pp 673–685 (1998) – reference: Savaux, J., Vion, J., Piechowiak, S., Mandiau, R., Matsui, T., Hirayama, K., Yokoo, M., Elmane, S., Silaghi, M.: Utilitarian approach to privacy in distributed constraint optimization problems. In: Proc. 30th FLAIRS, pp 454–459 (2017) – reference: Silaghi, M., Mitra, D.: Distributed constraint satisfaction and optimization with privacy enforcement. In: Intelligent Agent Technology (IAT), pp 531–535. IEEE (2004) – reference: Fluckiger, A., Verman, M., Berstein, A.: Improving approximate algorithms for dcops using ranks. In: International Workshop on Optimisation in Multi-Agent Systems, Singapore (2016) – reference: Hirayama, K., Yokoo, M.: The distributed breakout algorithms. In: Artificial Intelligence, vol. 161.1-2, pp 89–115 (2005) – reference: Walsh, T.: Stochastic constraint programming. In: Proceedings of the 15th european conference on artificial intelligence, pp 111–115. IOS Press (2002) – reference: Wallace, R.J., Freuder, E.C.: Anytime Algorithms for Constraint Satisfaction and SAT Problems. In: ACM SIGART Bulletin, vol. 7.2, pp 7–10 (1996) – reference: Petcu, A, Faltings, B: A scalable method for multiagent constraint optimization. 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Snippet	Privacy has traditionally been a major motivation of distributed problem solving. One popular approach to enable privacy in distributed environments is to...
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SubjectTerms	Algorithms Analysis Artificial Intelligence Complex Systems Computer Science Constraint modelling Costs Cryptography Mathematics Privacy Variables
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Title	Integrating domain and constraint privacy reasoning in the distributed stochastic algorithm with breakouts
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