NDSS

Geo-locating Drivers: A Study of Sensitive Data Leakage in Ride-Hailing Services

Qingchuan Zhao (The Ohio State University), Chaoshun Zuo (The Ohio State University), Giancarlo Pellegrino (CISPA, Saarland University; Stanford University), Zhiqiang Lin (The Ohio State University)

Increasingly, mobile application-based ride-hailing services have become a very popular means of transportation. Due to the handling of business logic, these services also contain a wealth of privacy-sensitive information such as GPS locations, car plates, driver licenses, and payment data. Unlike many of the mobile applications in which there is only one type of users, ride-hailing services face two types of users: riders and drivers. While most of the efforts had focused on the rider's privacy, unfortunately, we notice little has been done to protect drivers. To raise the awareness of the privacy issues with drivers, in this paper we perform the first systematic study of the drivers' sensitive data leakage in ride-hailing services. More specifically, we select $20$ popular ride-hailing apps including Uber and Lyft and focus on one particular feature, namely the nearby cars feature. Surprisingly, our experimental results show that large-scale data harvesting of drivers is possible for all of the ride-hailing services we studied. In particular, attackers can determine with high-precision the driver's privacy-sensitive information including mostly visited address (e.g., home) and daily driving behaviors. Meanwhile, attackers can also infer sensitive information about the business operations and performances of ride-hailing services such as the number of rides, utilization of cars, and presence on the territory. In addition to presenting the attacks, we also shed light on the countermeasures the service providers could take to protect the driver's sensitive information.