Pricing Utility vs. Location Privacy: A Differentially Private Data Sharing Framework for Ride-on-Demand Services

Noise perturbation introduced by differential privacy (DP) could degrade the quality of essential services like dynamic pricing and ride-matching in ride-on-demand (RoD) services. In this paper, we focus on RoD services under an honest-but-curious server, and propose a Pricing-Aware Differentially Private framework (PADP-RoD) to protect users’ location privacy while providing them with high-quality location-based services. Specifically, given that a price multiplier is subject to abrupt changes in response to shifts in supply and demand, especially near hotspots, we propose an adaptive supply and demand aware grid to capture the changes. Powered by the grid, we put forward two utility metrics for quantifying the quality loss of dynamic pricing and ride-matching services caused by perturbation, respectively. With those metrics, PADP-RoD is formulated as a minimization problem, aiming to minimize the quality loss of services given DP constraint. In this way, we can achieve an optimal balance between privacy and service quality. Due to the problem being a multi-objective optimization, we decompose it into a dynamic-pricing utility sub-problem and a ride-matching utility sub-problem, and solve them separately. To solve the dynamic pricing utility sub-problem, we propose a heuristic algorithm named the dynamic pricing mapping algorithm. Since the semi-infinite and non-differentiable nature of the ride-matching utility sub-problem, we transform this sub-problem into an unconstrained problem by the exact penalty function method, and solve it employing the particle swarm optimization algorithm. Our theoretical analysis demonstrates that PADP-RoD satisfies both $\varepsilon _{d}$-DP and $\varepsilon _{d}$-identifiability, and extensive experiments on a real-world dataset show that it can provide high-quality dynamic pricing and ride-matching services.