Efficient and Precise Secure Generalized Edit Distance and Beyond.

Abstract

Secure string-comparison by some non-linear metrics such as edit-distance and its variations is an important building block of many applications including patient genome matching and text-based intrusion detection. Despite the significance of these string metrics, computing them in a provably secure manner is very expensive. In this paper, we improve the performance of secure computation of these string metrics without sacrificing security, generality, composability, and accuracy. We explore a new design methodology that allows us to reduce the asymptotic cost by a factor of O(log n) (where n denotes the input string length). In our experiments, we observe up to an order-of-magnitude savings in time and bandwidth compared to the best prior results. We extended our semi-honest protocols to work in the malicious model, which is by-far the most efficient actively-secure protocols for computing these string metrics.