The large-scale collection of individuals’ mobility data poses serious privacy concerns. Instead of perturbing data by adding noise to the raw location data to preserve privacy of individuals, we propose an approach that achieves privacy-preservation at the statistics level of aggregating mobility datasets with the probabilistic data structure Count-Min Sketch (CMS) [1], which has been widely used to provide efficient statistic functions with a tunable error bound. We use CMS to estimate the population density distributions in the mobility datasets, where the error bound determines utility guarantees. We develop P4Mobi, a novel Probabilistic Privacy-Preserving Publishing framework for Mobility datasets that protects individuals’ privacy while complying to a specific utility requirement. We empirically validate the performance of P4Mobi in terms of utility and privacy-preservation by demonstrating its resilience against a recently proposed reconstruction attack model using two real-world datasets. We compare P4Mobi to two state-of-the-art methods and show that with the same level of privacy achieved against our attack model, P4Mobi significantly improves the utility of the published mobility datasets by up to $20\%$. We also provide a theoretical estimate of the utility achieved by P4Mobi. We found a very consistent match between the estimated and empirical utility of P4Mobi as evaluated on two datasets.

中文翻译：

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P4Mobi：用于发布移动数据集的概率隐私保护框架

个人移动数据的大规模收集带来了严重的隐私问题。我们不是通过向原始位置数据添加噪声以保护个人隐私来扰乱数据，而是提出了一种方法，该方法在具有概率数据结构的聚合移动性数据集的统计级别实现隐私保护Count-Min 草图 (内容管理系统) [1]，它已被广泛用于提供具有可调误差界限的高效统计函数。我们使用 CMS 来估计移动数据集中的人口密度分布，其中误差界限决定效用保证。我们开发P4Mobi， 一本小说 磷概率论的 磷利害关系-磷保留 磷发布框架 摩比liity 数据集，可在遵守特定实用程序要求的同时保护个人隐私。我们凭经验验证了P4Mobi通过使用两个真实世界的数据集展示其对最近提出的重建攻击模型的弹性，在效用和隐私保护方面。我们比较P4Mobi 两种最先进的方法，并表明在针对我们的攻击模型实现相同级别的隐私的情况下， P4Mobi 显着提高了已发布的移动数据集的效用高达 $20\%$. 我们还提供了对效用的理论估计P4Mobi. 我们发现估计效用和经验效用之间非常一致的匹配P4Mobi 在两个数据集上进行评估。