{"title":"Identification via location-profiling in GSM networks","authors":"Yoni De Mulder, G. Danezis, L. Batina, B. Preneel","doi":"10.1145/1456403.1456409","DOIUrl":null,"url":null,"abstract":"As devices move within a cellular network, they register their new location with cell base stations to allow for the correct forwarding of data. We show it is possible to identify a mobile user from these records and a pre-existing location profile, based on previous movement. Two different identification processes are studied, and their performances are evaluated on real cell location traces. The best of those allows for the identification of around 80% of users. We also study the misidentified users and characterise them using hierarchical clustering techniques. Our findings highlight the difficulty of anonymizing location data, and firmly establish they are personally identifiable.","PeriodicalId":74537,"journal":{"name":"Proceedings of the ACM Workshop on Privacy in the Electronic Society. ACM Workshop on Privacy in the Electronic Society","volume":"118 1","pages":"23-32"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"121","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ACM Workshop on Privacy in the Electronic Society. ACM Workshop on Privacy in the Electronic Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1456403.1456409","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 121
Abstract
As devices move within a cellular network, they register their new location with cell base stations to allow for the correct forwarding of data. We show it is possible to identify a mobile user from these records and a pre-existing location profile, based on previous movement. Two different identification processes are studied, and their performances are evaluated on real cell location traces. The best of those allows for the identification of around 80% of users. We also study the misidentified users and characterise them using hierarchical clustering techniques. Our findings highlight the difficulty of anonymizing location data, and firmly establish they are personally identifiable.