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Promoting location privacy... one lie at a time
If you have a question about this talk, please contact Eiko Yoneki.
Nowadays companies increasingly aggregate location data from different sources on the Internet to offer location-based services such as estimating current road traffic conditions, and finding the best nightlife locations in a city. However, these services have also caused outcries over privacy issues. As the volume of location data being aggregated expands, the comfort of sharing one’s whereabouts with the public at large will unavoidably decrease. Existing ways of aggregating location data in the privacy literature are largely centralized in that they rely on a trusted location-based service. Instead, we propose a piece of software (SpotME) that can run on a mobile phone and allows privacy-conscious users of location-based services to report, in addition to their actual locations, also some erroneous locations. The erroneous locations are selected by a randomized response algorithm in a way that makes it possible to accurately collect and process aggregated location data without affecting the fidelity of the result. We evaluate the accuracy of SpotME in estimating the number of people in a certain location upon two very different realistic mobility traces: the mobility of vehicles in urban, suburban and rural areas, and the mobility of subway train passengers in Greater London. We find that erroneous locations have little effect on the estimations (in both traces, the error is below 18% for a situation in which more than 99% of the locations are erroneous), yet they guarantee that users cannot be localized with high probability. Also, the computational and storage overheads for a mobile phone running SpotME are negligible, and the communication overhead is limited (SpotME adds an overhead of 21 byte/s).
This talk is part of the Computer Laboratory Systems Research Group Seminar series.
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