Enhancing Privacy Preservation of Anonymous Location Sampling Techniques in Traffic Monitoring Systems

Abstract

Automotive traffic monitoring belongs to a class of applications that collect aggregate statistics from the location traces of a large number of users. A widely-accepted belief is that anonymization of individual records can address the privacy problem which such aggregate statistics might pose. However, in this paper, we show that data mining techniques, such as clustering, can reconstruct private information from such anonymous traces. To meet this new challenge, we propose enhanced privacy-preserving algorithm to control the release of location traces near origins/destinations and evaluate it using real-world GPS location traces