Context-Aware Metric Differential Privacy for Vehicle Trajectory Data

Abstract

Metric Differential Privacy (mDP) generalizes differential privacy by allowing privacy guarantees to be expressed with respect to an arbitrary distance metric over secrets. While mDP has been adopted in geo-location protection, most existing mechanisms perturb each location record in isolation and do not model how contextual information (e.g., recent mobility history) affects the utility of the released data. This mismatch is particularly pronounced for vehicle mobility traces, where service quality often depends on temporally correlated locations. In this paper, we propose Context-aware mDP (C-mDP), a framework for vehicle location privacy that incorporates contextual dependencies into both the utility model and the privacy notion. C-mDP treats the protected secret as a context-augmented record and enforces metric indistinguishability over this augmented domain. We formulate optimal C-mDP mechanism design as a linear program (LP) that minimizes expected utility loss subject to C-mDP constraints. To improve scalability, we exploit conditional-independence structure between the current location and contextual variables to derive a reduced formulation with substantially fewer decision variables and constraints. We evaluate C-mDP on real-world vehicle mobility datasets and compare it with standard mDP baselines. The results show that C-mDP consistently achieves higher utility under the same privacy budget while satisfying the required metric privacy guarantees.