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Sharing without caring: privacy protection of users' spatio-temporal data without compromise on utility

Taxi demand prediction is an essential process for enabling efficient taxi operations and customer satisfaction. However, most existing solutions are vulnerable to leakage of passengers' privacy or membership inference attack. In this study, we propose a privacy-preserving taxi-demand system built without real customer data. Specifically, we employ LSTM-GANs to generate synthetic trajectories reflecting the typical Spatio-temporal semantics of the original data without privacy leakage. The system evaluation was held on a real taxi service provider in Japan for six months. The results show the ability of the system to keep privacy with 86% of the cases with barely a negligible decrease in the prediction performance compared to using the original data.