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Location Privacy
Modern phones are increasingly equipped with WiFi, Bluetooth, GPS, and accelerometers. These new interfaces let mobile applications gather contextual information to provide richer user experience. In our work, we evaluate the threat to location privacy of these new applications and design mechanisms to preserve location privacy. We consider centralized scenarios in which the system can be globally optimized and distributed scenarios with rational mobile nodes.
- J. Freudiger*, H.M. Manshaei*, J.-Y. Le Boudec, and J.-P. Hubaux. On the Age of Pseudonyms in Mobile Ad Hoc Networks. In IEEE Infocom, 2010.
- R. Shokri, J. Freudiger, M. Jadliwala, and J.-P. Hubaux. A Distortion-based Metric for Location Privacy. In ACM Workshop on Privacy in the Electronic Society (WPES), 2009.
- J. Freudiger, M. H. Manshaei, J.-P. Hubaux, and D. C. Parkes. On Non-cooperative Location Privacy: A Game-theoretic Analysis. In ACM Conference on Computer and Communications Security (CCS), 2009.
- J. Freudiger, M. Raya, and J.-P. Hubaux. Self-Organized Anonymous Authentication in Mobile Ad Hoc Networks. In Conference on Security and Privacy in Communication Networks, pages 350-372, 2009.
- J. Freudiger, R. Shokri, and J.-P. Hubaux. On the Optimal Placement of Mix Zones. In Privacy Enhancing Technologies Symposium (PETS), pages 216-234, 2009.
- J. Freudiger, M. Raya, M. Félegyházi, P. Papadimitratos, and J.-P. Hubaux. Mix-Zones for Location Privacy in Vehicular Networks. In ACM Workshop on Wireless Networking for Intelligent Transportation Systems (WiN-ITS), Vancouver, 2007.
- G. Calandriello, P. Papadimitratos, A. Lloy, and J.-P. Hubaux, Efficient and Robust Pseudonymous Authentication in VANET. In Proceedings of VANET 2007, September 2007
Data Privacy
The apparition of social applications on the Internet encourages users to share personal data online. For example, recommender systems find items potentially interesting to users of the service based on the user profile. Similarly, online advertisers track users across websites to provide more accurate advertisement. We analyze the threat to user privacy caused by such applications and propose various privacy-preserving mechanisms. Intuitively, we protect user privacy by relying on peer-to-peer communications between users of the service and on the use of multiple identities.
- R. Shokri, P. Pedarsani, G. Theodorakopoulos, and J.-P. Hubaux. Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles. In The 3rd ACM Conference on Recommender Systems (RecSys), New York, NY, USA, October 22-25, 2009.
- J. Freudiger, N. Vratonjic, and J.-P. Hubaux. Towards Privacy-Friendly Online Advertising. In IEEE Web 2.0 Security and Privacy, 2009.
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