{"title":"频谱滤波在保护隐私数据挖掘中的应用","authors":"Songtao Guo, Xintao Wu","doi":"10.1145/1141277.1141418","DOIUrl":null,"url":null,"abstract":"Randomization has been a primary tool to hide sensitive private information during privacy preserving data mining. The previous work based on spectral filtering, show the noise may be separated from the perturbed data under some conditions and as a result privacy can be seriously compromised. In this paper, we explicitly assess the effects of perturbation on the accuracy of the estimated value and give the explicit relation on how the estimation error varies with perturbation. In particular, we derive one upper bound for the Frobenius norm of reconstruction error. This upper bound may be exploited by attackers to determine how close their estimates are from the original data using spectral filtering technique, which imposes a serious threat of privacy breaches.","PeriodicalId":269830,"journal":{"name":"Proceedings of the 2006 ACM symposium on Applied computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":"{\"title\":\"On the use of spectral filtering for privacy preserving data mining\",\"authors\":\"Songtao Guo, Xintao Wu\",\"doi\":\"10.1145/1141277.1141418\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Randomization has been a primary tool to hide sensitive private information during privacy preserving data mining. The previous work based on spectral filtering, show the noise may be separated from the perturbed data under some conditions and as a result privacy can be seriously compromised. In this paper, we explicitly assess the effects of perturbation on the accuracy of the estimated value and give the explicit relation on how the estimation error varies with perturbation. In particular, we derive one upper bound for the Frobenius norm of reconstruction error. This upper bound may be exploited by attackers to determine how close their estimates are from the original data using spectral filtering technique, which imposes a serious threat of privacy breaches.\",\"PeriodicalId\":269830,\"journal\":{\"name\":\"Proceedings of the 2006 ACM symposium on Applied computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"28\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2006 ACM symposium on Applied computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1141277.1141418\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2006 ACM symposium on Applied computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1141277.1141418","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the use of spectral filtering for privacy preserving data mining
Randomization has been a primary tool to hide sensitive private information during privacy preserving data mining. The previous work based on spectral filtering, show the noise may be separated from the perturbed data under some conditions and as a result privacy can be seriously compromised. In this paper, we explicitly assess the effects of perturbation on the accuracy of the estimated value and give the explicit relation on how the estimation error varies with perturbation. In particular, we derive one upper bound for the Frobenius norm of reconstruction error. This upper bound may be exploited by attackers to determine how close their estimates are from the original data using spectral filtering technique, which imposes a serious threat of privacy breaches.