A General Privacy Loss Aggregation Framework for Distributed Constraint Reasoning

Distributed constraint solving are useful in tackling constrained problems when agents are not allowed to share his/her private information to others and/or gathering all necessary information to solve the problem in a centralized manner is infeasible. With these two limitations, distributed algorithms solve the problem by coordinating agents to negotiate with each other. However, once information is exchanged during negotiation, the private information may be leaked from one agent to another. We propose and design a framework based on Valuation of Possible States (VPS) to evaluate how well a distributed algorithm preserves the totality of all private information onthe entire system when solving distributed constraint optimization problems, by allowing the uses of different aggregators aggregating agents´ individual privacy loss. Two classes of aggregators: idempotent aggregators and risk based aggregators are proposed. We further proposed generalized inference rules to infer privacy loss of individual agents. We implement our work on four distributed constraint solving algorithms: Synchronous Branch and Bound (SynchBB), Asynchronous Distributed Constraint Optimization (ADOPT), Branch and Bound ADOPT (BnB-ADOPT), and Distributed Pseudo-tree Optimization Procedure (DPOP). Preliminary experimental evaluations on two benchmarks, Distributed Multi-Event Scheduling Problem (DiMES) and Random Distributed COP, comparing the four algorithms are performed.