Protecting Spatiotemporal Event Privacy in Continuous Location-Based Services

Abstract

Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting users' location privacy by releasing a perturbed location to third parties such as location-based service providers. However, when a user's perturbed locations are released continuously, existing LPPMs may not protect the sensitive information about the user's spatiotemporal activities, such as "visited hospital in the last week" or "regularly commuting between Address 1 and Address 2" (it is easy to infer that Addresses 1 and 2 may be home and office), which we call it spatiotemporal event. In this paper, we first formally define spatiotemporal event as Boolean expressions between location and time predicates, and then we define -spatiotemporal event privacy by extending the notion of differential privacy. Second, to understand how much spatiotemporal event privacy that existing LPPMs can provide, we design computationally efficient algorithms to quantify the privacy leakage of state-of-the-art LPPMs when an adversary has prior knowledge of the user's initial probability over possible locations. It turns out that the existing LPPMs cannot adequately protect spatiotemporal event privacy. Third, we propose a framework, PriSTE, to transform an existing LPPM into one protecting spatiotemporal event privacy against adversaries with any prior knowledge. Our experiments on real-life and synthetic data verified that the proposed method is effective and efficient.

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

“…be employed to protect user's location privacy in social distancing scenarios. In [135], a location privacy-preserving method leveraging spatio-temporal events of mobile users in continuous location-based services, e.g., office visitation, is investigated. Specifically, an -differential privacy is designed to protect spatio-temporal events against attackers by adding random noise to the event data [138]- [140].…”

Privacy for 5G-Supported Vehicular Networks

“…Therefore, trajectory privacy [134], [135], [136] refers to location indistinguishability and trajectory indistinguishability. Given that locations are correlated [137], it is more difficult to protect trajectory privacy.…”

“…It is well-acknowledged to utilize differential privacy [121] to perturb the real location to a noisy one. However, enforcing differential privacy in 5GVN has to address three issues in real deployment, i.e., correlation with activity [137], repeated submission [199], [200], and utility. Similar to identity privacy, if the inner correlation with activity is exposed, users' locations are endangered.…”

Mobile User Location Inference Attacks Fusing with Multiple Background Knowledge in Location-Based Social Networks

“…However, due to the lack of effective and safe data management, users' privacy is under the risk of leakage. Many research efforts have been devoted to investigating location privacy protection [3][4][5][6].…”