关键词: K-匿名, 隐私保护, 预测攻击, 滑动窗口

Abstract: K-anonymity for location privacy is widely applied in LBS privacy protection. However, most of the researches based on K-anonymity mechanism do not consider the attackers’ background knowledge. Thus, a dynamic anonymity algorithm for resisting the trajectory prediction attack is proposed. The similar historical trajectories of the user’s base trajectory are picked out using the sliding-window constraint. Then the trajectories are used to predict the next location of the user. Further, neighboring historical data is added to the vulnerable locations to resist the prediction attack. Compared with the similar algorithm, experimental results show that the algorithm has better prediction performance, and reduces the privacy disclosure risk under the same privacy requirements.

Key words: K-anonymity, privacy protection, prediction attack, sliding-window