Sharing sensitive data is vital in enabling many modern data analysis and machine learning tasks. However, current methods for data release are insufficiently accurate or granular to provide meaningful utility, and they carry a high risk of deanonymization or membership inference attacks. In this paper, we propose a differentially private synthetic data generation solution with a focus on the compelling domain of location data. We present two methods with high practical utility for generating synthetic location data from real locations, both of which protect the existence and true location of each individual in the original dataset. Our first, partitioning-based approach introduces a novel method for privately generating point data using kernel density estimation, in addition to employing private adaptations of classic statistical techniques, such as clustering, for private partitioning. Our second, network-based approach incorporates public geographic information, such as the road network of a city, to constrain the bounds of synthetic data points and hence improve the accuracy of the synthetic data. Both methods satisfy the requirements of differential privacy, while also enabling accurate generation of synthetic data that aims to preserve the distribution of the real locations. We conduct experiments using three large-scale location datasets to show that the proposed solutions generate synthetic location data with high utility and strong similarity to the real datasets. We highlight some practical applications for our work by applying our synthetic data to a range of location analytics queries, and we demonstrate that our synthetic data produces near-identical answers to the same queries compared to when real data is used. Our results show that the proposed approaches are practical solutions for sharing and analyzing sensitive location data privately.

中文翻译：

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真实世界中保护隐私的合成位置数据

共享敏感数据对于实现许多现代数据分析和机器学习任务至关重要。然而，当前的数据发布方法不够准确或粒度不足以提供有意义的效用，并且它们具有去匿名化或成员推理攻击的高风险。在本文中，我们提出了一种差异私有合成数据生成解决方案，重点是位置数据的引人注目的领域。我们提出了两种具有很高实用性的方法，用于从真实位置生成合成位置数据，这两种方法都保护原始数据集中每个人的存在和真实位置。我们的第一个基于分区的方法除了采用经典统计技术的私有改编外，还引入了一种使用内核密度估计私下生成点数据的新方法，例如集群，用于私有分区。我们的第二种基于网络的方法结合了公共地理信息，例如城市的道路网络，以限制合成数据点的边界，从而提高合成数据的准确性。这两种方法都满足了差分隐私的要求，同时还能够准确生成旨在保留真实位置分布的合成数据。我们使用三个大规模位置数据集进行实验，以表明所提出的解决方案生成的合成位置数据具有高实用性和与真实数据集的高度相似性。通过将我们的合成数据应用于一系列位置分析查询，我们突出了我们工作的一些实际应用，并且我们证明，与使用真实数据时相比，我们的合成数据对相同的查询产生了几乎相同的答案。我们的结果表明，所提出的方法是私密共享和分析敏感位置数据的实用解决方案。