{"title":"Differential Privacy in the Local Setting","authors":"Ninghui Li","doi":"10.1145/3180445.3190667","DOIUrl":null,"url":null,"abstract":"Differential privacy has been increasingly accepted as the de facto standard for data privacy in the research community. While many algorithms have been developed for data publishing and analysis satisfying differential privacy, there have been few deployment of such techniques.","PeriodicalId":355181,"journal":{"name":"Proceedings of the Fourth ACM International Workshop on Security and Privacy Analytics","volume":"SE-12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Fourth ACM International Workshop on Security and Privacy Analytics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3180445.3190667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Differential privacy has been increasingly accepted as the de facto standard for data privacy in the research community. While many algorithms have been developed for data publishing and analysis satisfying differential privacy, there have been few deployment of such techniques.