Privacy-preserving personalized pricing and matching for ride hailing platforms

This research addresses the growing concern of balancing personalized services with data privacy in the ride-hailing industry. While personalized pricing and matching strategies, fueled by travelers’ personal data, can optimize platform revenue, they also expose users and platforms to significant privacy risks. The correlation between personalized pricing, waiting times, and personal information might be exploited by third-party agents to infer sensitive user attributes, resulting in potential economic losses for the platform and severe consequences for users, including compromised privacy and potential discrimination. Existing privacy protection methods often fall short in providing robust and quantifiable guarantees. To overcome these limitations, this study introduces a privacy-preserving approach for personalized pricing and matching within ride-hailing platforms. The proposed approach leverages the bounded Laplace (BL) mechanism and parallel composition to inject noise into the order price and waiting time feedback provided to travelers. This study rigorously demonstrates that the proposed approach satisfies differential privacy. Furthermore, the proposed approach outperforms other classic privacy-preserving methods in terms of platform revenue. This superior performance is validated through extensive numerical experiments using realistic ride-hailing data.