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

• Published in: IEEE transactions on dependable and secure computing Vol. 22; no. 4; pp. 3497 - 3513
• Main Authors: Zheng, Zhirun, Li, Zhetao, Long, Saiqin, Guo, Suiming, Chen, Chao, Xu, Ke
• Format: Journal Article
• Language: English
• Published: Washington IEEE 01.07.2025; IEEE Computer Society
• Subjects: Algorithms; Bayes methods; Computer privacy; differential privacy; dynamic pricing utility; Heuristic algorithms; Heuristic methods; Location based services; Location privacy; Loss measurement; Matching; Minimization; Multiple objective analysis; Particle swarm optimization; Penalty function; Perturbation; Perturbation methods; Pricing; Privacy; ride-matching utility; ride-on-demand (RoD) services; Servers; Supply & demand; Supply and demand; Vehicles

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 P...