Experimental analysis of privacy loss in DCOP algorithms

Rachel Greenstadt; Jonathan P. Pearce; Emma Bowring; Milind Tambe

doi:10.1145/1160633.1160899

Experimental analysis of privacy loss in DCOP algorithms

Rachel Greenstadt, Jonathan P. Pearce, Emma Bowring, Milind Tambe

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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. [3,4] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that early DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. Do state-of-the art DCOP algorithms suffer from a similar short-coming? This paper answers that question by investigating the most efficient DCOP algorithms, including both DPOP and ADOPT.

Original language	English (US)
Title of host publication	Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems
Pages	1424-1426
Number of pages	3
DOIs	https://doi.org/10.1145/1160633.1160899
State	Published - 2006
Event	Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS - Hakodate, Japan Duration: May 8 2006 → May 12 2006

Publication series

Name	Proceedings of the International Conference on Autonomous Agents
Volume	2006

Conference

Conference	Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Country/Territory	Japan
City	Hakodate
Period	5/8/06 → 5/12/06

Keywords

Constraint reasoning
DCOP
Privacy

ASJC Scopus subject areas

General Engineering

Access to Document

10.1145/1160633.1160899

Cite this

Experimental analysis of privacy loss in DCOP algorithms. / Greenstadt, Rachel; Pearce, Jonathan P.; Bowring, Emma et al.
Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems. 2006. p. 1424-1426 (Proceedings of the International Conference on Autonomous Agents; Vol. 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Greenstadt, R, Pearce, JP, Bowring, E & Tambe, M 2006, Experimental analysis of privacy loss in DCOP algorithms. in Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems. Proceedings of the International Conference on Autonomous Agents, vol. 2006, pp. 1424-1426, Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, Hakodate, Japan, 5/8/06. https://doi.org/10.1145/1160633.1160899

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title = "Experimental analysis of privacy loss in DCOP algorithms",

abstract = "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. [3,4] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that early DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. Do state-of-the art DCOP algorithms suffer from a similar short-coming? This paper answers that question by investigating the most efficient DCOP algorithms, including both DPOP and ADOPT.",

keywords = "Constraint reasoning, DCOP, Privacy",

author = "Rachel Greenstadt and Pearce, {Jonathan P.} and Emma Bowring and Milind Tambe",

year = "2006",

doi = "10.1145/1160633.1160899",

language = "English (US)",

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series = "Proceedings of the International Conference on Autonomous Agents",

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booktitle = "Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems",

note = "Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS ; Conference date: 08-05-2006 Through 12-05-2006",

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T1 - Experimental analysis of privacy loss in DCOP algorithms

AU - Greenstadt, Rachel

AU - Pearce, Jonathan P.

AU - Bowring, Emma

AU - Tambe, Milind

PY - 2006

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N2 - 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. [3,4] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that early DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. Do state-of-the art DCOP algorithms suffer from a similar short-coming? This paper answers that question by investigating the most efficient DCOP algorithms, including both DPOP and ADOPT.

AB - 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. [3,4] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that early DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. Do state-of-the art DCOP algorithms suffer from a similar short-coming? This paper answers that question by investigating the most efficient DCOP algorithms, including both DPOP and ADOPT.

KW - Constraint reasoning

KW - DCOP

KW - Privacy

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T3 - Proceedings of the International Conference on Autonomous Agents

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BT - Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems

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ER -

Experimental analysis of privacy loss in DCOP algorithms

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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