HTF: Homogeneous Tree Framework for Differentially-Private Release of Location Data
HTF: Homogeneous Tree Framework for Differentially-Private Release of Location Data
复制标题
HTF：用于位置数据的差分隐私发布的同质树框架
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
C. Shahabi
中科院分区：
文献类型：
--
作者：
Sina Shaham;G. Ghinita;Ritesh Ahuja;John Krumm;C. Shahabi
使用位置数据的移动应用程序无处不在，涵盖了交通、城市规划和医疗保健等领域。位置数据的重要用例依赖于统计查询，例如，识别用户工作和旅行的热点。通过构建直方图，可以有效地回答此类查询。然而，精确的直方图会暴露个人用户的敏感细节。差分隐私（DP）是一种成熟且被广泛采用的保护模型，但大多数符合DP的直方图方法以数据独立的方式工作，导致准确性较差。少数提出的数据依赖技术试图根据数据集特征调整直方图分区，但由于添加了实现DP所需的噪声，它们表现不佳。我们将密度同质性确定为驱动符合dp的直方图准确性的主要因素，并且我们构建了一个数据结构来分割空间，以便在每个结果分区内数据密度是同质的。我们通过大规模真实世界数据的大量实验表明，与现有方法相比，所提出的方法具有更高的精度。
Mobile apps that use location data are pervasive, spanning domains such as transportation, urban planning and healthcare. Important use cases for location data rely on statistical queries, e.g., identifying hotspots where users work and travel. Such queries can be answered efficiently by building histograms. However, precise histograms can expose sensitive details about individual users. Differential privacy (DP) is a mature and widely-adopted protection model, but most approaches for DP-compliant histograms work in a data-independent fashion, leading to poor accuracy. The few proposed data-dependent techniques attempt to adjust histogram partitions based on dataset characteristics, but they do not perform well due to the addition of noise required to achieve DP. We identify density homogeneity as a main factor driving the accuracy of DP-compliant histograms, and we build a data structure that splits the space such that data density is homogeneous within each resulting partition. We show through extensive experiments on large-scale real-world data that the proposed approach achieves superior accuracy compared to existing approaches.