Privacy-Preserving Route Matching: Simplifications and additions

Abstract

Due to recent security breaches in today’s common applications, privacy requirements for modern applications have become stricter. To fulfill these requirements, more and more research has been put into producing methods that preserve the privacy of users while retaining the same degree of functionality and efficiency. In this thesis, based on the privacy-preserving ridesharing model PrivatePool, two novel models are presented for preserving user’s privacy in a ridesharing application. The former model optimizes the algorithm of PrivatePool to increase its efficiency, while the latter model takes additional parameters into consideration when performing its computations. Finally, we conclude that an ad-hoc privacy-preserving algorithm outperforms a general solution and that the addition of more parameters to the ad-hoc model has a significant negative effect on its efficiency.