Yaowei Han, Yang Cao, Sheng Li, Qiang Ma, Masatoshi Yoshikawa
{"title":"Voice-Indistinguishability -- Protecting Voiceprint with Differential Privacy under an Untrusted Server","authors":"Yaowei Han, Yang Cao, Sheng Li, Qiang Ma, Masatoshi Yoshikawa","doi":"10.1145/3372297.3420025","DOIUrl":null,"url":null,"abstract":"With the rising adoption of advanced voice-based technology together with increasing consumer demand for smart devices, voice-controlled \"virtual assistants\" such as Apple's Siri and Google Assistant have been integrated into people's daily lives. However, privacy and security concerns may hinder the development of such voice-based applications since speech data contain the speaker's biometric identifier, i.e., voiceprint (as analogous to fingerprint). To alleviate privacy concerns in speech data collection, we propose a fast speech data de-identification system that allows a user to share her speech data with formal privacy guarantee to an untrusted server. Our open-sourced system can be easily integrated into other speech processing systems for collecting users' voice data in a privacy-preserving way. Experiments on public datasets verify the effectiveness and efficiency of the proposed system.","PeriodicalId":20481,"journal":{"name":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","volume":"100 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3372297.3420025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
With the rising adoption of advanced voice-based technology together with increasing consumer demand for smart devices, voice-controlled "virtual assistants" such as Apple's Siri and Google Assistant have been integrated into people's daily lives. However, privacy and security concerns may hinder the development of such voice-based applications since speech data contain the speaker's biometric identifier, i.e., voiceprint (as analogous to fingerprint). To alleviate privacy concerns in speech data collection, we propose a fast speech data de-identification system that allows a user to share her speech data with formal privacy guarantee to an untrusted server. Our open-sourced system can be easily integrated into other speech processing systems for collecting users' voice data in a privacy-preserving way. Experiments on public datasets verify the effectiveness and efficiency of the proposed system.