Privacy-Preserving Autonomous Cab Service Management Scheme

Abstract

In the autonomous vehicles era, vehicles will be an on-demand service rather than an owned product, i.e., many passengers will rely on Autonomous Cabs (ACs) in their transportation. In order to guarantee the high quality of the AC service, the AC company needs to learn the geographic distribution of the potential service requests. The best way to obtain this information is by requesting the passengers to frequently report their locations, e.g., by using their smart-phones. However, learning the passengers' locations causes a serious location privacy issue. In this paper, we propose a privacy-preserving scheme for reporting location information for AC management. Data aggregation approach is used to preserve location privacy by providing the AC company with the total number of requests in each geographic area, while hiding the individual reports of the passengers. Unlike the existing aggregation schemes that do binary data addition, the used aggregation scheme does individual bits addition. Our analysis and experimental results demonstrate that the proposed scheme is efficient and can preserve location privacy.