差异化私有轨迹数据发布
Differentially Private Trajectory Data Publication
摘要：
随着位置感知设备的日益普及，轨迹数据已经在各种应用领域中生成和收集。轨迹数据包含丰富的信息，对许多数据分析任务都很有用。然而，轨迹数据的不当发布和使用可能会危及个人隐私。然而，它已被证明，现有的隐私保护的轨迹数据发布方法，从基于分区的隐私模型，例如k-匿名，无法提供足够的隐私保护。本文以蒙特利尔地区公共交通机构Societe de transport de Montreal（STM）的数据发布为背景，研究了严格差分隐私模型下的轨迹数据发布问题。我们提出了一个有效的数据依赖性，但差异私人消毒算法，这是适用于不同类型的轨迹数据。我们的方法的效率来自自适应缩小输出域，通过建立一个嘈杂的前缀树的基础上的底层数据。此外，作为后处理步骤，我们利用前缀树的固有约束进行约束推理，这导致更好的实用性。这是第一篇介绍在差分隐私下发布大量轨迹数据的实用解决方案的论文。我们从计数查询和频繁序列模式挖掘的角度研究了净化数据的实用性。对STM真实轨迹数据的大量实验表明，我们的方法具有很高的实用性，并且可扩展到大型轨迹数据集。
英文摘要：
With the increasing prevalence of location-aware devices, trajectory data has been generated and collected in various application domains. Trajectory data carries rich information that is useful for many data analysis tasks. Yet, improper publishing and use of trajectory data could jeopardize individual privacy. However, it has been shown that existing privacy-preserving trajectory data publishing methods derived from partition-based privacy models, for example k-anonymity, are unable to provide sufficient privacy protection. In this paper, motivated by the data publishing scenario at the Societe de transport de Montreal (STM), the public transit agency in Montreal area, we study the problem of publishing trajectory data under the rigorous differential privacy model. We propose an efficient data-dependent yet differentially private sanitization algorithm, which is applicable to different types of trajectory data. The efficiency of our approach comes from adaptively narrowing down the output domain by building a noisy prefix tree based on the underlying data. Moreover, as a post-processing step, we make use of the inherent constraints of a prefix tree to conduct constrained inferences, which lead to better utility. This is the first paper to introduce a practical solution for publishing large volume of trajectory data under differential privacy. We examine the utility of sanitized data in terms of count queries and frequent sequential pattern mining. Extensive experiments on real-life trajectory data from the STM demonstrate that our approach maintains high utility and is scalable to large trajectory datasets.
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。
