Privacy Leakage From Dynamic Prices: Trip Purpose Mining as an Example

Dynamic prices are used in many scenarios, e.g., flight ticketing, hotel room booking and ride-on-demand (RoD) service such as Uber and DiDi, and while they are beneficial for service providers, practitioners or users, they lead to the concern of privacy leakage - the possibility of learning user information from dynamic prices. In this paper, we aim to study this possibility and choose trip purpose mining in RoD service as an attack example, based on real-world large datasets. We discuss the criteria of choosing datasets - ubiquitous, collective and easily accessible - from the perspective of an attacker, and extract features describing trip information, spatio-temporal and dynamic prices context. The trip purpose mining problem is then solved as a multi-class classification problem and multiple binary-class problems. In the multi-class problem, we verify that dynamic prices information results in a 17.1% improvement in classification accuracy; in the binary-class problems, we quantify feature contributions and explain the different extents of privacy leakage in identifying different trip purposes. Our hope is that the study not only serves as a case study demonstrating the privacy leakage problem in RoD service, but also sheds light on such privacy problem in other services using dynamic prices and triggers more research efforts.