Private and Decentralized Location Sharing for Congestion-Aware Routing

Data-driven methodologies offer promising ways to improve the efficiency, safety, and adaptability of modern and future mobility systems. While these methodologies offer many exciting upsides, they also introduce new challenges, particularly in the realm of user privacy. Specifically, data-driven algorithms often require user data, and the way this data is collected can pose privacy risks to end users. Centralized data sharing systems where a single entity (mobility service provider or transit network operator) collects and manages user data have a central point of failure. Users have to trust that this entity will not sell or use their data to infer sensitive private information. Unfortunately, in practice many advertising companies offer to buy such data for the sake of targeted advertisements. With this as motivation, we study the problem of using location data for congestion-aware routing in a privacy-preserving way. Rather than having users report their location to a central operator, we present a protocol in which users participate in a decentralized and privacy-preserving computation to estimate travel times for the roads in the network in a way that no individuals' location is ever observed by any other party. The protocol uses the Laplace mechanism in conjunction with secure multi-party computation to ensure that it is cryptogrpahically secure and that its output is differentially private. A natural question is if privacy necessitates degradation in accuracy or system performance. We show that if a road has sufficiently high capacity, then the travel time estimated by our protocol is provably close to the travel time estimated by the ground truth. We also evaluate the protocol through numerical experiments which show that the protocol provides privacy guarantees with minimal overhead to system performance.