Private Electric Vehicle Charging Location Aggregation Based on Local Differential Privacy

The charging location data generated by electric vehicles frequently accessing charging piles for charging are of great significance for optimizing the arrangement of charging piles and guiding the electric power dispatching. However, charging location data are private information for vehicle users. In order to prevent the leakage of the privacy of these users, it is urgent to explore a way of private charging location data aggregation. Therefore, a local differential privacy technology is adopted to preserve the charging location data of electric vehicles. A partition-based privacy preservation charging location data aggregation method is proposed by introducing Bayesian randomized multiple dummies algorithm. The method employs the Bayesian randomized multiple dummies algorithm to design a local obfuscation algorithm for locally perturbing a vehicle's charging location. Then, the private location aggregation method for charging location data with the characteristics of sparseness and small size samples is designed by combining reconstruction algorithm of the randomized multiple dummies algorithm. At the same time, under the premise of ensuring the level of privacy preservation, the whole location domain is divided to narrow the privacy location domain, thereby further improving the utility of aggregation result. The privacy analysis of the proposed method is given. Finally, experimental results on four different synthetic datasets, namely, uniform distribution, normal distribution, peak distribution and random distribution, as well as the public Gowalla dataset are carried out. The experimental results show that the proposed method is superior to the existing randomized projection matrix based private aggregation method in terms of utility under the same privacy level. © 2019, Editorial Department of Advanced Engineering Sciences. All right reserved.