A Privacy-Preserving Order Dispatch Scheme for Ride-Hailing Services

Abstract

The ride-hailing system has become popular around the world. The Service Providers (SPs) such as Uber and Didi dispatch passenger orders based on their location information. However, one concern from the public is whether the SPs could protect the location privacy of passengers. In this paper, we propose an order dispatch scheme that could preserve the location privacy of passengers based on their requirements. Our scheme uses cloaking regions in which the SPs cannot distinguish actual locations of passengers. The trade-off is the loss of matching performance or social welfare, i.e., the increase in the overall pick-up distance. We formulate the problem as maximizing the social welfare (or minimizing the overall pick-up distances) under privacy requirements of passengers. A bipartite-matching-based scheme is investigated, and we provide a theoretical bound on the matching performance under specific privacy requirements. Nevertheless, minimizing the overall pick-up distances does not consider the interest of each individual passenger. Passengers with low privacy requirements may be matched with drivers far from them. Therefore, we further propose a pricing scheme that could make up for the individual loss by allocating discounts on their riding fares. Especially, three discount allocation strategies are proposed in this paper. Experiments on both real-world and synthetic datasets show the efficiency of our scheme.