Ensuring location diversity in privacy preserving spatio-temporal data mining

Official URL: http://192.168.1.20/record=b1293850 (Table of Contents)

The rise of mobile technologies in the last decade has lead to vast amounts of location information generated by individuals. From the knowledge discovery point of view, this data is quite valuable as it has commercial value, but the inherent personal information in the data raises privacy concerns. There exist many algorithms in the literature to satisfy the privacy requirements of individuals, by generalizing, perturbing, and suppressing data. The algorithms that try to ensure a level of indistinguishability between trajectories in the dataset, fail when there is not enough diversity among sensitive locations visited by those users. We propose an approach that ensures location diversity named as (c,p)- confidentiality, which bounds the probability of visiting a sensitive location given the background knowledge of the adversary. Instead of grouping the trajectories, we anonymize the underlying map structure. We explain our algorithm and show the performance of our approach. We also compare the performance of our algorithm with an existing technique and show that location diversity can be satisfied efficiently.