Abstract：The suppression processing of published trajectory data can effectively reduce the risk of user privacy disclosure.In traditional methods，the method of global suppression processing of data sets to satisfy the LKC-privacy model reduces the availability of trajectory data.Therefore，this paper proposes an optimized suppression differential privacy protection algorithm for trajectory data publishing.The algorithm firstly makes effective local suppression judgment on the points in the minimum violation sequence of the trajectory data set，determines the suppression order according to the rejection priority score，and then updates the minimum violation sequence set to achieve the purpose of reducing frequent sequence loss rate and sequence loss rate in the trajectory data set.Secondly，according to the sensitive information after updating the minimum violation sequence set，establishing a classification tree and adding noise to the leaf node to improve the security of the data to be published.Experiments show that the proposed algorithm reduces the data loss rate effectively，improves the data availability and reduces the risk of privacy disclosure.
白雨靓,李晓会,陈潮阳,王亚君. 面向轨迹数据发布的优化抑制差分隐私保护研究[J]. 小型微型计算机系统, 2021, 42(8): 1787-1792.
BAI Yu-liang,LI Xiao-hui,CHEN Chao-yang,WANG Ya-jun. Research on Optimal Suppression Differential Privacy Protection for Trajectory Data Publishing. Journal of Chinese Computer Systems, 2021, 42(8): 1787-1792.
2021, Vol. 42 
