A profile anonymization model for location-based services

Location-based services (LBS) aim at delivering point of need information. Personalization and customization of such services, based on the profiles of mobile users, would significantly increase the value of such services. Since profiles may include sensitive information of mobile users and moreover can help identify a person, such customization is allowable only when the security and privacy policies dictated by them are respected. While LBS providers are presumed to be untrusted entities, the location services that capture and maintain mobile users' location to enable communication are considered trusted, and therefore can capture and manage the profile information. The question then is, how to enable the use of location based services while protecting privacy?In this paper, we address the problem of privacy preservation via anonymization. Prior research in this area attempts to ensure k-anonymity by generalizing the location. However, a person may still be identified based on his/her profile if the profiles of all k people in the generalized region are not the same. We extend the notion of k-anonymity by proposing a profile based k-anonymization model that guarantees anonymity even when profiles of mobile users are revealed to untrusted entities. Specifically, our anonymization methods generalize both location and profiles to the extent specified by the user. We propose a novel unified index structure, called the PTPR-tree to enhance the performance during anonymization. PTPR-tree is an extension of the TPR-tree [in: SIGMOD'00: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, ACM, 2000, pp. 331342] which organizes both the locations of mobile users as well as their profiles using a single index, and therefore can efficiently find candidate users for the proposed profile based anonymization models.