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Communication Dans Un Congrès Année : 2016

PrivCheck: privacy-preserving check-in data publishing for personalized location based services

Résumé

With the widespread adoption of smartphones, we have observed an increasing popularity of Location-Based Services (LBSs) in the past decade. To improve user experience, LBSs often provide personalized recommendations to users by mining their activity (i.e., check-in) data from location-based social networks. However, releasing user check-in data makes users vulnerable to inference attacks, as private data (e.g., gender) can often be inferred from the users'check-in data. In this paper, we propose PrivCheck, a customizable and continuous privacy-preserving check-in data publishing framework providing users with continuous privacy protection against inference attacks. The key idea of PrivCheck is to obfuscate user check-in data such that the privacy leakage of user-specified private data is minimized under a given data distortion budget, which ensures the utility of the obfuscated data to empower personalized LBSs. Since users often give LBS providers access to both their historical check-in data and future check-in streams, we develop two data obfuscation methods for historical and online check-in publishing, respectively. An empirical evaluation on two real-world datasets shows that our framework can efficiently provide effective and continuous protection of user-specified private data, while still preserving the utility of the obfuscated data for personalized LBSs
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Dates et versions

hal-01400175 , version 1 (21-11-2016)

Identifiants

Citer

Dingqi Yang, Daqing Zhang, Bingqing Qu, Philippe Cudré-Mauroux. PrivCheck: privacy-preserving check-in data publishing for personalized location based services. UBICOMP 2016 : ACM International Joint Conference on Pervasive and Ubiquitous Computing, Sep 2016, Heidelberg, Germany. pp.545 - 556, ⟨10.1145/2971648.2971685⟩. ⟨hal-01400175⟩
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