Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services

Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting users' location privacy by releasing a perturbed to third parties such as location-based service providers. However, when user's locations are released continuously, existing LPPMs may not protect the sensitive information about real-world activities, “visited hospital in last week” or “regularly commuting between A and B every weekday” (it is easy infer that be home office), which we call it spatiotemporal event. In this paper, first formally define event Boolean expressions time predicates, then ε-spatiotemporal extending notion of differential privacy. Second, understand how much can provide, design computationally efficient algorithms quantify leakage state-of-the-art LPPMs. It turns out adequately Third, propose framework, PriSTE, transform an LPPM into one calibrating LPPM's budgets. Our experiments on real-life synthetic data verified proposed method effective efficient.