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Collaborative Location Privacy (2011)
| Venue: | IN: 2011 IEEE 8TH INTERNATIONAL CONFERENCE ON MOBILE ADHOC AND SENSOR SYSTEMS, MASS (2011 |
| Citations: | 5 - 2 self |
BibTeX
@INPROCEEDINGS{Shokri11collaborativelocation,
author = {Reza Shokri and Panos Papadimitratos and George Theodorakopoulos and Jean-pierre Hubaux},
title = {Collaborative Location Privacy},
booktitle = {IN: 2011 IEEE 8TH INTERNATIONAL CONFERENCE ON MOBILE ADHOC AND SENSOR SYSTEMS, MASS (2011},
year = {2011},
publisher = {}
}
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Abstract
Location-aware smart phones support various location-based services (LBSs): users query the LBS server and learn on the fly about their surroundings. However, such queries give away private information, enabling the LBS to identify and track users. We address this problem by proposing the first, to the best of our knowledge, usercollaborative privacy preserving approach for LBSs. Our solution, MobiCrowd, is simple to implement, it does not require changing the LBS server architecture, and it does not assume third party privacy-protection servers; still, MobiCrowd significantly improves user location-privacy. The gain stems from the collaboration of MobiCrowd-ready mobile devices: they keep their context information in a buffer, until it expires, and they pass it to other users seeking such information. Essentially, the LBS does not need to be contacted unless all the collaborative peers in the vicinity lack the sought information. Hence, the user can remain hidden from the server, unless it absolutely needs to expose herself through a query. Our results show that MobiCrowd hides a high fraction of location-based queries, thus significantly enhancing user location-privacy. To study the effects of various parameters, such as the collaboration level and contact rate between mobile users, we develop an epidemic model. Our simulations with real mobility datasets corroborate our model-based findings. Finally, our implementation of MobiCrowd on Nokia platforms indicates that it is lightweight and the collaboration cost is negligible.
Keyphrases
collaborative location privacy user location-privacy mobile user collaboration cost lb server collaboration level private information mobicrowd-ready mobile device location-based query context information high fraction sought information various location-based service various parameter third party privacy-protection server nokia platform abstract location-aware smart phone collaborative peer lb server architecture usercollaborative privacy real mobility datasets model-based finding contact rate epidemic model
