Trajectory Privacy Preserving Based on Statistical Differential Privacy
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摘要: 随着车联网不断地发展,车联网为驾乘者提供便捷服务的同时，也带来了相应的隐私保护问题.轨迹数据发布将可能泄露用户位置隐私,从而危害用户人身安全；为改变已有差分隐私保护方法中添加随机噪音的弊端，提出一种基于统计差分隐私的轨迹隐私保护方法.车辆行驶轨迹具有Markov过程的特点，根据车辆轨迹的特征计算轨迹中位置节点敏感度；并根据位置敏感度，统计阈值和敏感度阈值添加适量Laplace噪音；使用平均相对误差评价轨迹数据的可用性大小.实验证实了基于统计差分隐私的轨迹隐私保护方法的可用性和有效性.Abstract: With the continuous development of Internet of vehicles, Internet of vehicles provides the convenient services to drivers and passengers. But it also brings some new problems of privacy protection. The existing methods for trajectory data publishing may leak users' location privacy. Thus, it may endanger the users' personal safety. In order to avoid the drawbacks of adding random noise in the existing methods for differential privacy protection, we propose a novel method for trajectory privacy protection based on statistical differential privacy. At first, one can calculate the sensitivity of position nodes in vehicle traces according to the characteristics of traces since there are some characteristics of Markov process in vehicle traces. And then, one can add some moderate Laplace noises according to the sensitivity of position nodes, statistical threshold and sensitivity threshold. As a result, the new method is obtained. Evaluating the availability of the trajectory data through the average relative error, the experimental results verify the availability and effectiveness of the proposed approach for privacy preserving based on statistical differential privacy.
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Keywords:
- trajectory data /
- differential privacy /
- Markov process /
- data publishing /
- privacy protection
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