基于非敏感信息分析的轨迹数据隐私保护发布

针对轨迹数据发布时轨迹和非敏感信息引起的隐私泄露问题，提出一种基于非敏感信息分析的轨迹数据隐私保护发布算法。首先，分析轨迹和非敏感信息的关联性构建轨迹隐私泄露判定模型，得到最小违反序列元组（MVS），然后借鉴公共子序列的思想，在消除MVS带来的隐私泄露风险时，选择MVS中对轨迹数据损失最小的时序序列作为抑制对象，从而生成具有隐私能力和低数据损失率的匿名轨迹数据集。仿真实验结果表明，与LKC-Local算法和Trad-Local算法相比，在序列长度为3的情况下，该算法平均实例损失率分别降低了6%和30%，平均最大频繁序列（MFS）损失率分别降低了7%和60%，因此所提算法能够有效用于提高推荐服务质量。

Privacy-preserving trajectory data publishing based on non-sensitive information analysis

Focusing on the issue of privacy disclosure between trajectory and non-sensitive information, a trajectory privacy preserving algorithm based on non-sensitive information analysis was proposed. Firstly, the correlation between trajectory and non-sensitive information was analyzed to build trajectory privacy disclosure decision model, and the Minimal Violating Sequence tuple (MVS) was gotten. Secondly, using common subsequences, the doublets with the minimal loss of trajectory data in MVS were selected as the suppression objects when removing the privacy risks caused by MVS, then the anonymized trajectory dataset with privacy and low data loss was obtained. In the comparison experiments with LKC-Local algorithm and Trad-Local algorithm, when the sequence length is 3, the average instance loss of the proposed algorithm is decreased by about 6% and 30% respectively, and the average MFS (Maximal Frequent Sequence) loss is decreased by about 7% and 60% respectively. The experimental results verify that the proposed algorithm can effectively improve the quality of recommend service.