基于本地化差分隐私的电动汽车接入充电点位置隐私保护

LOCATION PRIVACY PROTECTION BASED ONDIFFERENTIAL PRIVACY FOR V2G NETWORKS

针对现有电动汽车接入充电点位置的隐私保护算法不可抵御背景知识攻击和不可信第三方的隐私攻击问题,提出一种基于本地化差分隐私的电动汽车接入充电点位置隐私保护方法。使用基于距离变换的栅格算法对充电点分布构建维诺图并编号;在客户端对每辆电动汽车所在充电点位置数据进行K-RR随机响应,使结果满足本地化差分隐私,并提供一种在扰动结果上获得电动汽车计数分布无偏估计的方法;通过实验证明该方法在真实数据中与k-匿名方式在查询误差率相当的情况下,其算法安全性及效率更佳。

Aiming at the problem that the existing location privacy protection algorithm for electric vehicle cannot resist background knowledge attack and untrusted third-party privacy attack when accessing to the charging point,a location privacy protection method based local differential privacy was proposed.A distance-based raster algorithm was used to construct the Voronoi diagram and number the charging points.K-RR random response method that satisfies the local differential privacy was used to perturb the charging point position data of each electric vehicle,and a method to obtain an unbiased estimation of the electric vehicle counting distribution was proposed.Experiments show that the proposed method is safer and more efficient in the real data compared with the k-anonymous method in the case of the same query error rate.