Trajectory data often provides useful information that can be utilized in real-life applications, such as traffic planning and location-based advertising. Because people’s trajectory information can result in serious personal privacy leakage, trajectory privacy protection methods are employed. However, existing methods assume and use the same privacy requirements for all trajectories, which affect privacy protection efficiency and data utilization. This paper proposes a trajectory privacy protection method based on user requirement. By dividing different time intervals, it sets different privacy protection parameters for different trajectories to provide more detailed privacy protection. The proposed method utilizes the divided time intervals and privacy protection requirements to form a privacy requirement matrix, to construct an anonymous trajectory equivalence class and undirected graph. Then, trajectories are processed to form anonymous sets. Euclidean distance is also replaced with Manhattan distance in calculating the distance of the trajectories, which would improve the privacy protection and data utility and narrow the gap between the theoretical privacy protection and the actual protective effects. Comparative experiments demonstrate that the proposed method outperforms other similar methods in regards to both privacy protection and data utilization.

中文翻译：

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基于用户需求的个性化轨迹隐私保护方法

轨迹数据通常提供有用的信息，可用于现实生活中的应用，例如交通规划和基于位置的广告。由于人们的轨迹信息会导致严重的个人隐私泄露，因此采用了轨迹隐私保护方法。然而，现有方法对所有轨迹假设和使用相同的隐私要求，这影响了隐私保护效率和数据利用率。提出一种基于用户需求的轨迹隐私保护方法。通过划分不同的时间间隔，为不同的轨迹设置不同的隐私保护参数，提供更细致的隐私保护。该方法利用划分的时间间隔和隐私保护要求形成隐私要求矩阵，构造匿名轨迹等价类和无向图。然后，轨迹被处理以形成匿名集。在计算轨迹距离时，欧几里得距离也替换为曼哈顿距离，这样可以提高隐私保护和数据效用，缩小理论隐私保护与实际保护效果之间的差距。对比实验表明，所提出的方法在隐私保护和数据利用方面都优于其他类似方法。这将提高隐私保护和数据实用性，缩小理论隐私保护与实际保护效果之间的差距。对比实验表明，所提出的方法在隐私保护和数据利用方面都优于其他类似方法。这将提高隐私保护和数据实用性，缩小理论隐私保护与实际保护效果之间的差距。对比实验表明，所提出的方法在隐私保护和数据利用方面都优于其他类似方法。