Secure computation of randomized functions

TLDR: In this article, the authors considered two user secure computation of randomized functions, where only one user computes the output and both the users are semi-honest, and the computation is such that no user learns any additional information about the other user's input and output other than what cannot be inferred from its own inputs and output.

Abstract: Two user secure computation of randomized functions is considered, where only one user computes the output. Both the users are semi-honest; and computation is such that no user learns any additional information about the other user's input and output other than what cannot be inferred from its own input and output. First we consider a scenario, where privacy conditions are against both the users. In perfect security setting, Kilian gave a characterization of securely computable randomized functions in [1], and we provide rate-optimal protocols for such functions. We prove that the same characterization holds in asymptotic security setting as well and give a rate-optimal protocol. In another scenario, where privacy condition is only against the user who is not computing the function, we provide rate-optimal protocols. For perfect security in both the scenarios, our results are in terms of chromatic entropies of different graphs. In asymptotic security setting, we get single-letter expressions of rates in both the scenarios.