On the Vulnerability and Generality of K–Anonymity Location Privacy Under Continuous LBS Requests

Abstract

With the development of personal communication devices, location-based services have been widely used. However, the risk of location information leakage is a fundamental problem that prevents the success for these applications. Recently, some location-based privacy protection schemes have been proposed, among which K-anonymity scheme is the most popular one. However, as we empirically demonstrated, these schemes may not preserve satisfactory effect in trajectory-aware scenarios. In particular, we propose a new attack model using public navigation services. According to the empirical results, the attack algorithm correlates a series of snapshots associated with continuous queries, eliminating some of the less likely routes, and seriously undermining the anonymity of the query, thereby increasing the probability of attack. In order to defend against the proposed attacks, two enhanced versions of K-anonymity mechanism are proposed for this attack model, which further protects the user’s trajectory privacy.

Hanbo Dai and Hui Li are co-first authors and contribute equally to this work. This work is granted by National Natural Science Foundation of China (No. 61672408, 61972309) and National Engineering Laboratory (China) for Public Safety Risk Perception and Control by Big Data (PSRPC).