M. Barhamgi, Mu Yang, Chia-Mu Yu, Y. Yu, A. Bandara, D. Benslimane, B. Nuseibeh
{"title":"Enabling End-Users to Protect their Privacy","authors":"M. Barhamgi, Mu Yang, Chia-Mu Yu, Y. Yu, A. Bandara, D. Benslimane, B. Nuseibeh","doi":"10.1145/3052973.3055154","DOIUrl":null,"url":null,"abstract":"In this paper we present our ongoing work to build an approach to empower users of IoT-based cyber physical systems to protect their privacy by themselves. Our approach allows users to identify the privacy risks involved in sharing private data with a data consumer, assess the value of their private data based on identified risks and take a pragmatic data sharing decision balancing the risks with the benefits generated by the sharing. Our approach features a knowledgebase, called the Privacy Oracle, that exploits the power of the Semantic Web to determine how raw metadata can be combined by data consumers to infer privacy-sensitive information as well as the privacy risks associated with the disclosure of inferred information.","PeriodicalId":20540,"journal":{"name":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","volume":"29 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2017-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3052973.3055154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
In this paper we present our ongoing work to build an approach to empower users of IoT-based cyber physical systems to protect their privacy by themselves. Our approach allows users to identify the privacy risks involved in sharing private data with a data consumer, assess the value of their private data based on identified risks and take a pragmatic data sharing decision balancing the risks with the benefits generated by the sharing. Our approach features a knowledgebase, called the Privacy Oracle, that exploits the power of the Semantic Web to determine how raw metadata can be combined by data consumers to infer privacy-sensitive information as well as the privacy risks associated with the disclosure of inferred information.