Constrained-Based Differential Privacy for Mobility Services

Ubiquitous mobile and wireless communication systems have the potential to revolutionize transportation systems, making accurate mobility traces and activity-based patterns available to optimize the design and operations of mobility systems. However, these rich data sets also pose significant privacy risks, potentially revealing highly sensitive information about individual agents. This paper studies how to use differential privacy to release mobility data for transportation applications. It shows that existing approaches do not provide the desired fidelity for practical uses. To remedy this limitation, the paper proposes the idea of Constraint-Based Differential Privacy (CBDP) that casts the production of a private data set as an optimization problem that redistributes the noise introduced by a randomized mechanism to satisfy fundamental constraints of the original data set. The CBDP has strong theoretical guarantees: It is a constant factor away from optimality and when the constraints capture categorical features, it runs in polynomial time. Experimental results show that CBDP ensures that a city-level multi-modal transit system has similar performance measures when designed and optimized over the real and private data sets and improves state-of-art privacy methods by an order of magnitude.