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Combinatorial optimization is a fundamental problem found in many fields. Inmany real life situations, the constraints and the objective function formingthe optimization problem are naturally distributed amongst different sites insome fashion. A typical approach for solving such problem is to collect all ofthis information together and centrally solve the problem. However, thisrequires all parties to completely share their information, which may lead toserious privacy issues. Thus, it is desirable to propose a privacy preservingtechnique that can securely solve specific combinatorial optimization problems.A further complicating factor is that combinatorial optimization problems aretypically NP-hard, requiring approximation algorithms or heuristics to providea practical solution. In this paper, we focus on a very well-known hard problem-- the distributed graph coloring problem, which has been utilized to modelmany real world problems in scheduling and resource allocation. We proposeefficient protocols to securely solve such fundamental problem. We analyze thesecurity of our approach and experimentally demonstrate the effectiveness ofour approach.

Securely Solving the Distributed Graph Coloring Problem