Tianyi Feng, L. Wong, Sumei Sun, Yonghao Zhao, Zhixiang Zhang
{"title":"Location Privacy Preservation and Location-based Service Quality Tradeoff Framework Based on Differential Privacy","authors":"Tianyi Feng, L. Wong, Sumei Sun, Yonghao Zhao, Zhixiang Zhang","doi":"10.1109/WPNC47567.2019.8970184","DOIUrl":null,"url":null,"abstract":"With the widespread use of location-based services and the development of localization systems, user’s locations and even sensitive information can be easily accessed by some untrusted entities, which means privacy concerns should be taken seriously. In this paper, we propose a differential privacy framework to preserve users’ location privacy and provide location-based services. We propose the metrics of location privacy, service quality and differential privacy to introduce a location privacy preserving mechanism, which can help users find the tradeoff or optimal strategy between location privacy and service quality. In addition, we design an adversary model to infer users’ true locations, which can be used by application service providers to improve service quality. Finally, we present simulation results and analyze the performance of our proposed system.","PeriodicalId":284815,"journal":{"name":"2019 16th Workshop on Positioning, Navigation and Communications (WPNC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 16th Workshop on Positioning, Navigation and Communications (WPNC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WPNC47567.2019.8970184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
With the widespread use of location-based services and the development of localization systems, user’s locations and even sensitive information can be easily accessed by some untrusted entities, which means privacy concerns should be taken seriously. In this paper, we propose a differential privacy framework to preserve users’ location privacy and provide location-based services. We propose the metrics of location privacy, service quality and differential privacy to introduce a location privacy preserving mechanism, which can help users find the tradeoff or optimal strategy between location privacy and service quality. In addition, we design an adversary model to infer users’ true locations, which can be used by application service providers to improve service quality. Finally, we present simulation results and analyze the performance of our proposed system.