The Location Privacy Protection of Electric Vehicles with Differential Privacy in V2G Networks

• Main Authors: Yuancheng Li, Pan Zhang, Yimeng Wang
• Format: Article
• Language: English
• Published: MDPI AG 2018-10-01
• Series: Energies
• Subjects: electric vehicle (EV); location privacy protection; differential privacy; random sampling algorithm; sparse vector technology; vehicle to grid (V2G)
• Online Access: http://www.mdpi.com/1996-1073/11/10/2625

Vehicle-to-grid (V2G) is an important component of smart grids and plays a significant role in improving grid stability, reducing energy consumption and generating cost. However, while electric vehicles are being charged, it is possible to expose the location and movement trajectories of the electric vehicles, thereby triggering a series of privacy and security issues. In response to this problem, we propose a new quadtree-based spatial decomposition algorithm to protect the location privacy of electric vehicles. First of all, we use a random sampling algorithm, which is based on differential privacy, to obtain enough spatial data to achieve the balance between large-scale spatial data and the amount of noise. Secondly, in order to overcome the shortcomings of using tree height to control Laplacian noise in the quadtree, we use sparse vector technology to control the noise added to the tree nodes. Finally, according to the vehicle-to-grid network structure in the smart grid, we propose a location privacy protection model based on distributed differential privacy technology for EVs in vehicle-to-grid networks. We demonstrate application of the proposed model in real spatial data and show that it can achieve the best effect on the security of the algorithm and the availability of data.