Multi-user Location Correlation Protection with Differential Privacy

In the big data era, with the rapid development of location-based applications, GPS enabled devices and big data institutions, location correlation privacy raises more and more people's concern. Because adversaries may combine location correlations with their background knowledge to guess users' privacy, such correlation should be protected to preserve users' privacy. In order to deal with the location disclosure problem, location perturbation and generalization have been proposed. However, most proposed approaches depend on syntactic privacy models without rigorous privacy guarantee. Furthermore, many approaches only consider perturbing the locations of one user without considering multi-user location correlations, so these techniques cannot prevent various inference attacks well. Currently, differential privacy has been regarded as a standard for privacy protection, but there are new challenges for applying differential privacy in the location correlations protection. The privacy protection not only should meet the needs of users who request location-based services, but also should protect location correlation among multiple users. In this paper, we propose a systematic solution to protect location correlations privacy among multiple users with rigorous privacy guarantee. First of all, we propose a novel definition, private candidate sets which are obtained by hidden Markov models. Then, we quantify the location correlation between two users by using the similarity of hidden Markov models. Finally, we present a private trajectory releasing mechanism which can preserve the location correlations among users who move under hidden Markov models in a period of time. Experiments on real-world datasets also show that multi-user location correlation protection is efficient.