{"title":"Privacy preservation of aggregates in hidden databases: why and how?","authors":"A. Dasgupta, Nan Zhang, Gautam Das, S. Chaudhuri","doi":"10.1145/1559845.1559863","DOIUrl":null,"url":null,"abstract":"Many websites provide form-like interfaces which allow users to execute search queries on the underlying hidden databases. In this paper, we explain the importance of protecting sensitive aggregate information of hidden databases from being disclosed through individual tuples returned by the search queries. This stands in contrast to the traditional privacy problem where individual tuples must be protected while ensuring access to aggregating information. We propose techniques to thwart bots from sampling the hidden database to infer aggregate information. We present theoretical analysis and extensive experiments to illustrate the effectiveness of our approach.","PeriodicalId":344093,"journal":{"name":"Proceedings of the 2009 ACM SIGMOD International Conference on Management of data","volume":"78 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2009 ACM SIGMOD International Conference on Management of data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1559845.1559863","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23
Abstract
Many websites provide form-like interfaces which allow users to execute search queries on the underlying hidden databases. In this paper, we explain the importance of protecting sensitive aggregate information of hidden databases from being disclosed through individual tuples returned by the search queries. This stands in contrast to the traditional privacy problem where individual tuples must be protected while ensuring access to aggregating information. We propose techniques to thwart bots from sampling the hidden database to infer aggregate information. We present theoretical analysis and extensive experiments to illustrate the effectiveness of our approach.