具有局部差分隐私的真实世界轨迹共享
Real-World Trajectory Sharing with Local Differential Privacy
摘要：
共享轨迹对于许多现实世界的应用都是有益的，例如通过接触者追踪来管理疾病传播，以及根据人口的旅行模式来定制公共服务。然而，公众对隐私和数据保护的关注限制了这些数据的共享程度。局部差分隐私使得能够进行数据共享，其中用户共享其数据的扰动版本，但是现有机制未能结合用户独立的公共知识（例如，营业地点和营业时间、公共交通时刻表、地理定位的推文）。这种限制使得机制限制性太强，给出不切实际的输出，并最终导致实际效用低。为了解决这些问题，我们提出了一种局部差异私有机制，该机制基于扰动分层结构的重叠$n$ -gram（即，长度为$n$的连续序列）。我们的机制使用一个多维的层次结构，在公开的外部知识的真实世界的地方的利益，以提高现实主义和实用性的扰动，共享的轨迹。重要的是，包括真实世界的公共数据不会对隐私或效率产生负面影响。我们的实验，使用真实世界的数据和一系列的查询，每个与真实世界的应用程序类似物，证明了我们的方法的优越性，在一系列的替代方法。
英文摘要：
Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited the extent to which this data is shared. Local differential privacy enables data sharing in which users share a perturbed version of their data, but existing mechanisms fail to incorporate user-independent public knowledge (e.g., business locations and opening times, public transport schedules, geo-located tweets). This limitation makes mechanisms too restrictive, gives unrealistic outputs, and ultimately leads to low practical utility. To address these concerns, we propose a local differentially private mechanism that is based on perturbing hierarchically-structured, overlapping $n$-grams (i.e., contiguous subsequences of length $n$) of trajectory data. Our mechanism uses a multi-dimensional hierarchy over publicly available external knowledge of real-world places of interest to improve the realism and utility of the perturbed, shared trajectories. Importantly, including real-world public data does not negatively affect privacy or efficiency. Our experiments, using real-world data and a range of queries, each with real-world application analogues, demonstrate the superiority of our approach over a range of alternative methods.
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