When You Say (DCOP) Privacy, What do You Mean? - Categorization of DCOP Privacy and Insights on Internal Constraint Privacy
نویسنده
چکیده
Privacy preservation is a main motivation for using the DCOP model and as such, it has been the subject of comprehensive research. The present paper provides for the first time a categorization of all possible DCOP privacy types. The paper focuses on a specific type, internal constraint privacy, which is highly relevant for models that enable asymmetric payoffs (PEAV-DCOP and ADCOP). An analysis of the run of two algorithms, one for ADCOP and one for PEAV, reveals that both models lose some internal constraint privacy.
منابع مشابه
An Experimental Analysis of privacy loss in DCOP algorithms
Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. Unfortunately, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking despite the fact that agent privacy is a key motivation for applying DCOPs in many applications. Recently, Maheswaran et al. [1, 2] introduced a framework for quantitative evalua...
متن کاملAnalysis of Privacy Loss in Distributed Constraint Optimization
Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. However, despite agent privacy being a key motivation for applying DCOPs in many applications, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking. Recently, [Maheswaran et al.2005] introduced a framework for quantitative evaluations of privacy ...
متن کاملSSDPOP: Using Secret Sharing to Improve the Privacy of DCOP
Multi-agent systems designed to work collaboratively with groups of people typically require private information that people will entrust to them only if they have assurance that this information will be protected. Distributed Constraint Optimization (DCOP) has emerged as a prominent technique for multiagent coordination, but existing algorithms for solving DCOP problems do not adeqately protec...
متن کاملP-SyncBB: A Privacy Preserving Branch and Bound DCOP Algorithm
Distributed constraint optimization problems enable the representation of many combinatorial problems that are distributed by nature. An important motivation for such problems is to preserve the privacy of the participating agents during the solving process. The present paper introduces a novel privacy-preserving branch and bound algorithm for this purpose. The proposed algorithm, P-SyncBB, pre...
متن کاملPrivacy Preserving Implementation of the Max-Sum Algorithm and its Variants
One of the basic motivations for solving DCOPs is maintaining agents’ privacy. Thus, researchers have evaluated the privacy loss of DCOP algorithms and defined corresponding notions of privacy preservation for secured DCOP algorithms. However, no secured protocol was proposed for Max-Sum, which is among the most studied DCOP algorithms. As part of the ongoing effort of designing secure DCOP alg...
متن کامل