A Random Anonymity Framework for Location Privacy

Location k-anonymity techniques typically use anony- mous spatial region to ensure privacy. But these solutions are vul- nerable to multiple queries attacks and inference attacks. Failing to account for the obstacle in geographic space is a severe problem since adversaries will surely regard these constraints. A novel framework is proposed to enhance location-dependent queries, based on the theoretical work of k-anonymity and Voronoi diagrams, allows a user to express service requirement and privacy require- ment by specifying a region and an appropriate value ofk. A trusted anonymity server form a restricted set （k, r, s）, which is composed of a number of discrete points to meet the requirements for location k-anonymity and location /-diversity. The location-based services （LBS） server implements an efficient algorithm for continu- ous-region-query processing. Simulation results demonstrated that the framework is superior to previous works in terms of privacy. Moreover, discreteness and randomness of the anonymous set are conducive to resisting location tracking attacks.