Privacy-preserving ride clustering for customized-bus sharing: A fog-assisted approach

Abstract

Customized-bus Sharing Service (CSS) enables a centralized server to schedule comfortable bus trips for users by ride clustering based on the individual requirements. It has been increasingly popular in crowded metropolises, bringing a lot of convenience and reducing trip costs to users. Ride clustering is essential for the server to determine the stops of a customized bus, but it also leads to the exposure of users' current locations and spatio-temporal patterns. Although privacy-preserving ride clustering can generate optimal bus routes, it depends on frequent interactions between users and the server, so all the users should be always online. In this paper, we propose a privacy-preserving ride clustering scheme for CSS to support off-line users, in which fog computing is introduced to assist the server in generating bus route without the exposure of users' travel plans. Fog servers are able to perform ride clustering interacting with the server, after receiving the preferred pick-up and drop-off positions from users. Thus, the users are unnecessary to be always online. In addition, the Paillier cryptosystem and randomization technique are leveraged to protect the user's privacy without sacrificing the clustering quality. Finally, the proposed privacy-preserving ride clustering scheme is demonstrated to have the advantage of low computational and communication overhead with high security guarantees.