$k^{τ,ε}$ -匿名：面向时空轨迹数据的隐私保护发布
$k^{τ,ε}$-anonymity: Towards Privacy-Preserving Publishing of Spatiotemporal Trajectory Data
摘要：
移动网络运营商可以通过被动或主动监控设备位置来跟踪用户。记录的轨迹为大型用户群体的活动提供了前所未有的前景，这使得开发新的网络解决方案和服务成为可能，并扩大了跨研究学科的研究。然而，个人轨迹的披露引起了严重的隐私问题：因此，这些数据往往受到限制性的非披露协议的保护，限制了其可用性并阻碍了潜在的使用。在本文中，我们有助于发展的技术解决方案的隐私保护发布的移动用户的时空轨迹的问题。我们提出了一种算法，概括的数据，使他们满足$k^{τ,ε}$ -匿名，原始的隐私标准，阻止攻击的轨迹。与现实世界的数据集的评估表明，我们的算法达到了其目标，同时保留了相当程度的准确性的数据。我们的工作是朝着开放的，隐私保护的时空轨迹数据集的方向迈出了一步。
英文摘要：
Mobile network operators can track subscribers via passive or active monitoring of device locations. The recorded trajectories offer an unprecedented outlook on the activities of large user populations, which enables developing new networking solutions and services, and scaling up studies across research disciplines. Yet, the disclosure of individual trajectories raises significant privacy concerns: thus, these data are often protected by restrictive non-disclosure agreements that limit their availability and impede potential usages. In this paper, we contribute to the development of technical solutions to the problem of privacy-preserving publishing of spatiotemporal trajectories of mobile subscribers. We propose an algorithm that generalizes the data so that they satisfy $k^{τ,ε}$-anonymity, an original privacy criterion that thwarts attacks on trajectories. Evaluations with real-world datasets demonstrate that our algorithm attains its objective while retaining a substantial level of accuracy in the data. Our work is a step forward in the direction of open, privacy-preserving datasets of spatiotemporal trajectories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
