The vehicle fuel consumption gauge is a vehicle’s basic device that usually records the instan- taneous as well as average fuel consumption of the vehicle, which brings a lot of convenience during the driving process. The individual real-time fuel consumption data are meaningless, but its continuous real-time fuel consumption data contains some information that may reveal the user’s privacy, which is sensitive information for users. There are hot topics on privacy protection of sensitive user information, however, most of the studies have focused on privacy protection at the time of data release and the need for a trusted third party. In this paper, we propose a negative survey-based approach that can be utilized to protect aggregated vehicle fuel consumption data against time-series-based differential attacks. Its anonymous algorithm on the user side and es- timation algorithm on the server side are able to protect the user’s privacy regardless of changes in the user’s fuel consumption data. The experiment results show that our method can achieve better privacy protection with better application prospects in a simpler and more effective way.

车辆油耗表是车辆的基本设备，通常会记录车辆的瞬时和平均油耗，这在驾驶过程中带来了很多便利。各个实时油耗数据是没有意义的，但是其连续的实时油耗数据包含一些可能会泄露用户隐私的信息，这对用户而言是敏感信息。关于敏感用户信息的隐私保护有许多热门话题，但是，大多数研究都集中在数据发布时的隐私保护以及对可信第三方的需求上。在本文中，我们提出了一种基于否定调查的方法，该方法可用于保护汇总的车辆燃料消耗数据免受基于时间序列的差分攻击。无论用户的油耗数据有何变化，其在用户端的匿名算法和在服务器端的估计算法均能够保护用户的隐私。实验结果表明，该方法能够以更简单，更有效的方式实现更好的隐私保护，具有更好的应用前景。