Center for Discrete Mathematics and Theoretical Computer Science

About: Center for Discrete Mathematics and Theoretical Computer Science is a facility organization based out in Piscataway, New Jersey, United States. It is known for research contribution in the topics: Local search (optimization) & Optimization problem. The organization has 140 authors who have published 175 publications receiving 2345 citations.

Private Inference Control for Aggregate Database Queries

Abstract: We study private inference control for aggregate queries, such as those provided by statistical databases or modern database languages, to a database in a way that satisfies privacy requirements and inference control requirements. For each query, the client learns the value of the function for that query if and only if the query passes a specified in- ference control rule. The server learns nothing about the queries, and the client learns nothing other than the query output for passing queries. We present general protocols for aggregate queries with private inference control.

Optimal, Distributed Decision-Making: The Case of No Communication

Abstract: We present a combinatorial framework for the study of a natural class of distributed optimization problems that involve decision-making by a collection of n distributed agents in the presence of incomplete information; such problems were originally considered in a load balancing setting by Papadimitriou and Yannakakis (Proceedings of the 10th Annual ACM Symposium on Principles of Distributed Computing, pp. 61-64, August 1991). For any given decision protocol and assuming no communication among the agents, our framework allows to obtain a combinatorial inclusion-exclusion expression for the probability that no "overflow" occurs, called the winning probability, in terms of the volume of some simple combinatorial polytope. Within our general framework, we offer a complete resolution to the special cases of oblivious algorithms, for which agents do not "look at" their inputs, and non-oblivious algorithms, for which they do, of the general optimization problem. In either case, we derive optimality conditions in the form of combinatorial polynomial equations. For oblivious algorithms, we explicitly solve these equations to show that the optimal algorithm is simple and uniform, in the sense that agents need not "know" n. Most interestingly, we show that optimal non-oblivious algorithms must be non-uniform: we demonstrate that the optimality conditions admit different solutions for particular, different "small" values of n; however, these solutions improve in terms of the winning probability over the optimal, oblivious algorithm. Our results demonstrate an interesting trade-off between the amount of knowledge used by agents and uniformity for optimal, distributed decision-making with no communication.