论文标题
基于频率的随机化来保证空间轨迹的差异隐私
Frequency-based Randomization for Guaranteeing Differential Privacy in Spatial Trajectories
论文作者
论文摘要
随着基于GPS的设备的流行，已经不断收集大量的轨迹数据，并开发了各种基于位置的服务，这极大地使我们的日常生活受益。但是，释放的轨迹也引起了人们对个人隐私的严重关注，最近的一些研究表明，空间轨迹中存在个人识别信息。由于隐私保护和公用事业保存之间的权衡，轨迹匿名化是非平地的。此外，在当前文献中尚未对恢复攻击进行很好的研究。为了解决这些问题，我们提出了一个基于频率的随机模型，并具有严格的差异隐私保证，以确保轨迹数据发布。特别是，我们引入了两种随机机制，以通过注射拉普拉斯噪声来扰动轨迹中显着重要位置的局部/全局频率分布。我们设计了一个分层索引以及一种新颖的搜索算法，以支持有效的轨迹修改，确保修改后的轨迹满足了不损害隐私保证或数据实用程序的扰动分布。对现实世界轨迹数据集的广泛实验验证了我们方法在抵抗个人重新识别和恢复攻击方面的有效性，同时保留了理想的数据实用程序以及实践中的可行性。
With the popularity of GPS-enabled devices, a huge amount of trajectory data has been continuously collected and a variety of location-based services have been developed that greatly benefit our daily life. However, the released trajectories also bring severe concern about personal privacy, and several recent studies have demonstrated the existence of personally-identifying information in spatial trajectories. Trajectory anonymization is nontrivial due to the trade-off between privacy protection and utility preservation. Furthermore, recovery attack has not been well studied in the current literature. To tackle these issues, we propose a frequency-based randomization model with a rigorous differential privacy guarantee for trajectory data publishing. In particular, we introduce two randomized mechanisms to perturb the local/global frequency distributions of significantly important locations in trajectories by injecting Laplace noise. We design a hierarchical indexing along with a novel search algorithm to support efficient trajectory modification, ensuring the modified trajectories satisfy the perturbed distributions without compromising privacy guarantee or data utility. Extensive experiments on a real-world trajectory dataset verify the effectiveness of our approaches in resisting individual re-identification and recovery attacks and meanwhile preserving desirable data utility as well as the feasibility in practice.
