Privacy Preserving Car Parking: A distributed approach

There has been a substantial interest recently in privacy preserving problems in various application domains, including data publishing, data mining, classification, secret voting, private querying of database, playing mental poker, and many others. The main constraint is that entities involved in the system are unwilling to reveal the data they hold or make them public. However, they may want to collaborate and find the solution of a bigger computational problem without revealing the privately held data. There are several approaches for addressing such issues, including cryptographic methods, transformation methods, and parallel and distributed computation techniques. In this thesis, these three methods are highlighted and a greater emphasis is placed on the last one. A particular interesting application, namely the car parking problem, or parking slot assignment problem. To solve the problem in a privacy preserving manner, a new parallel and distributed computation method is addressed. The goal is to allocate the parking slots to the intended destinations of the car drivers, but without revealing anyone else the intended destinations. The result is a decentralized privacy preserving car parking algorithm. The proposed algorithm is compared to three other methods in terms of computational complexity. It is shown that the algorithm offers good performance as long as the entities involved have a collaborative approach.