Anonymity preserving framework for location-based information services
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
The VLDB Journal — The International Journal on Very Large Data Bases
A Privacy Preserving Method Using Privacy Enhancing Techniques for Location Based Services
Mobile Networks and Applications
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Protecting privacy of mobile users of location based services is a currently interesting research problem. Most protection techniques can be categorized into either those providing location privacy or those guaranteeing k-anonymity. A mobile user (i) has location privacy if, when he makes an LBS request, adversaries cannot tell his location precise enough to cause privacy concerns, and (ii) has k-anonymity if adversaries cannot distinguish him, among a group of k users, as the definite request issuer. SpaceTwist proposed recently[15] is in the former category, but makes no attempt to provide k- anonymity. The purpose of this paper is to study a method that makes SpaceTwist provide k-anonymity in addition to location privacy. The extended algorithm is called AnonTwist. The major challenge is the ability to make sure that at least k users are in the privacy area given in the SpaceTwist algorithm, i.e., in the so-called “twisted space”. AnonTwist contains two technical contributions. The first is a user density map in the form of a Quadtree so that we have an estimate of the number of users in each spatial area. The second is a nontrivial counting mechanism, over the density map, to keep track of the number of users in the twisted space. Comparison of AnonTwist and SpaceTwist is performed via an experimental evaluation. The results show that the performance of AnonTwist is comparable to that of SpaceTwist. With the additional advantage of providing k-anonymity, AnonTwist should be the more favorable algorithm to use in practice.