Characterizing Optimal Security And Round-Complexity For Secure Or Evaluation

Secure multi-party computation allows mutually distrusting parties to compute securely over their private data. However, even in the semi-honest two-party setting, most interesting functions cannot be computed securely in the information theoretic plain model. Intuitively, the objective of accurately evaluating the output of such functions is inherently inimical to the privacy concerns of the parties. Securely evaluating OR of the input bits of two parties is the simplest example, and captures the essence of the hardness in securely evaluating most functions.This work studies the interplay between accuracy and privacy of secure 2-party function evaluation in the information-theoretic plain model. We provide an optimal accuracy versus privacy tradeoff for computing OR(x, y), where x and y are, respectively, the private input bits of Alice and Bob. In particular, we construct a round-optimal two-party protocol for OR that has maximum semi-honest security in the information-theoretic plain model. Prior results exhibit only weak tradeoffs that are far from the optimal. We generalize our techniques to obtain a tight accuracy versus-privacy tradeoff characterization for a stronger notion of security, namely differentially-private semi-honest security.The technical heart of our result is a new technique to derive inequalities for distributions of transcripts generated by protocols. This approach reduces the domain of the optimization problem from an unbounded number of transcripts to a constant size while preserving the optimal solution to the original problem. We believe that these techniques for analyzing protocols in the information-theoretic plain model will be of independent interest.