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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1012-2443
1573-7470
DOI:10.1007/s10472-021-09735-5