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Efficient and Precise Secure Generalized Edit Distance and Beyond
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DOI：10.1109/TDSC.2020.2984219.png)
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Abstract
En 中文
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 article, 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(logn) (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 have also extended our semi-honest protocols to work in the malicious model.
Keywords:
Secure string matching
privacy-preserving genome comparison
secure edit-distance
secure Needleman-Wunsch
secure LCS
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