{"title":"Efficient and Precise Secure Generalized Edit Distance and Beyond.","authors":"Ruiyu Zhu, Yan Huang","doi":"10.1109/tdsc.2020.2984219","DOIUrl":"10.1109/tdsc.2020.2984219","url":null,"abstract":"<p><p>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 <i>O</i>(log <i>n</i>) (where <i>n</i> 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.</p>","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":"19 1","pages":"579-590"},"PeriodicalIF":7.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/tdsc.2020.2984219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9270884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}