基于LFP树与与代理向量的轨迹隐私数据发布

TRAJECTORY PRIVACY DATA PUBLISHING BASED ON LFP TREE AND PROXY VECTOR

  • 摘要: 为了兼顾数据的安全性以及利用率,提出一种基于局部频繁模式树与与代理向量的轨迹隐私数据发布方法。引入一种基于网格环境的代理向量,从而有效避免隐私的泄露问题;根据基于信任用户的数据公开,可以预测整个轨迹流,并且能够得到特定区域的轨迹统计信息;引入局部频繁模式树,可以有效地跳过大量不必要的候选序列,并降低数据维数,减少时间复杂度。实验结果表明该方法能够保证良好的安全性与数据利用率。

     

    Abstract: In order to give consideration to the security and utilization of data, a privacy data publishing method based on local frequent pattern tree and proxy vector is proposed. A proxy vector based on grid environment was introduced to avoid privacy leakage. According to the data disclosure based on trusted users, the whole trajectory flow could be predicted, and the trajectory statistics of a specific region could be obtained. The local frequent pattern tree was introduced, which could effectively skip a large number of unnecessary candidate sequences, reduce the data dimension and time complexity. Experimental results show that the proposed method can ensure good security and data utilization.

     

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