Distributed constraint satisfaction and optimization with privacy enforcement

Silaghi, Marius; Mitra, Debasis

doi:10.1109/iat.2004.1343012

Cited by 37 publications

(55 citation statements)

References 6 publications

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“…Silaghi et al proposed algorithms for distributed constraint satisfaction and optimization with privacy enforcement (MPC-DisCSP) (Silaghi, 2003) (Silaghi & Mitra, 2004). Actually, MPC-DisCSP is based on a SFE technique (Ben-Or et al, 1988).…”

Section: R E L a Te D Wor K Smentioning

confidence: 99%

Privacy-Preserving Local Search for the Traveling Salesman Problem

Sakuma¹,

Kobayashi²

2010

Traveling Salesman Problem, Theory and Applications

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Section: R E L a Te D Wor K Smentioning

confidence: 99%

Privacy-Preserving Local Search for the Traveling Salesman Problem

Sakuma¹,

Kobayashi²

2010

Traveling Salesman Problem, Theory and Applications

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“…Traditionally, DCOP algorithms tolerate some loss in privacy for the sake of efficiency. However, Silaghi [40] coined the term "semi-cooperative algorithms" to describe DCOP algorithms that required agents to attain global optimality while maintaining privacy. Yokoo et al [49] looked at how to use multiple external server agents to find the global optimal while guaranteeing significant privacy.…”

Section: Multiple Objectives Within Dcopsmentioning

confidence: 99%

“…if any gT hresh il has changed, reset lb,ub (40) else store lb,ub (41) else if context compatible with CurrentContext and gT hresh il (42) store lb and ub (43) backTrack procedure backTrack (44) if xi not a virtual variable (45) if…”

Section: Combining Techniquesmentioning

confidence: 99%

Balancing local resources and global goals in multiply-constrained DCOP1

Tambe

Yokoo

2010

MGS

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Distributed constraint optimization (DCOP) is a useful framework for cooperative multiagent coordination. DCOP focuses on optimizing a single team objective. However, in many domains, agents must satisfy constraints on resources consumed locally while optimizing the team goal. Yet, these resource constraints may need to be kept private. Designing DCOP algorithms for these domains requires managing complex trade-offs in completeness, scalability, privacy and efficiency.This article defines the multiply-constrained DCOP (MC-DCOP) framework and provides complete (globally optimal) and incomplete (locally optimal) algorithms for solving MC-DCOP problems. Complete algorithms * This paper significantly extends our previous conference paper [3]. It extends that paper by providing all the incomplete algorithms from [2], as well as additional algorithms, more detailed explanation of these new algorithms, additional experimental results and analysis, and a detailed discussion of related work. 1 find the best allocation of scarce resources while optimizing the team objective, while incomplete algorithms are more scalable. The algorithms use four main techniques: (i) transforming constraints to maintain privacy; (ii) dynamically setting upper bounds on resource consumption; (iii) identifying the extent to which the local graph structure allows agents to compute exact bounds; and (iv) using a virtual assignment to flag problems rendered unsatisfiable by resource constraints. Proofs of correctness are presented for all algorithms. Experimental results illustrate the strengths and weaknesses of both the complete and incomplete algorithms.

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“…Silaghi and Rajeshirke [4] show that a secure combinatorial problem solver must necessarily pick the result randomly among optimal solutions to be really 1 denotes ≤, = or ≥. 2 [5] propose arithmetic circuits for solving constraint optimization problems that are exponential in the number of variables for any constraint graph. A significantly more efficient optimization protocol specialized on generalized Vickrey auctions and based on dynamic programming is proposed by Suzuki and Yokoo [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Distributed Linear Programming with Limited Disclosure

Yuan

Vaidya

2011

Lecture Notes in Computer Science

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Abstract. In today's networked world, resource providers and consumers are distributed globally and locally. However, with resource constraints, optimization is necessary to ensure the best possible usage of such scarce resources. Distributed linear programming (DisLP) problems allow collaborative agents to jointly maximize profits (or minimize costs) with a linear objective function while conforming to several shared as well as local linear constraints. Since each agent's share of the global constraints and the local constraints generally refer to its private limitations or capacities, serious privacy problems may arise if such information is revealed. While there have been some solutions proposed that allow secure computation of such problems, they typically rely on inefficient protocols with enormous communication cost. In this paper, we present a secure and extremely efficient protocol to solve DisLP problems where constraints are arbitrarily partitioned and no variable is shared between agents. In the entire protocol, each agent learns only a partial solution (about its variables), but learns nothing about the private input/output of other agents, assuming semi-honest behavior. We present a rigorous security proof and communication cost analysis for our protocol and experimentally validate the costs, demonstrating its robustness.

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Distributed constraint satisfaction and optimization with privacy enforcement

Abstract: [12,7,3,4,11]. In [7]

Cited by 37 publications

References 6 publications

Privacy-Preserving Local Search for the Traveling Salesman Problem

Privacy-Preserving Local Search for the Traveling Salesman Problem

Balancing local resources and global goals in multiply-constrained DCOP1

Efficient Distributed Linear Programming with Limited Disclosure

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