Differentially private location trace synthesis (DPLTS) has recently emerged as a solution to protect mobile users' privacy while enabling the analysis and sharing of their location traces. A key challenge in DPLTS is to best preserve the utility in location trace datasets, which is non-trivial considering the high dimensionality, complexity and heterogeneity of datasets, as well as the diverse types and notions of utility. In this paper, we present OptaTrace: a utility-optimized and targeted approach to DPLTS. Given a real trace dataset D, the differential privacy parameter epsilon controlling the strength of privacy protection, and the utility/error metric Err of interest; OptaTrace uses Bayesian optimization to optimize DPLTS such that the output error (measured in terms of given metric Err) is minimized while epsilon-differential privacy is satisfied. In addition, OptaTrace introduces a utility module that contains several built-in error metrics for utility benchmarking and for choosing Err, as well as a front-end web interface for accessible and interactive DPLTS service. Experiments show that OptaTrace's optimized output can yield substantial utility improvement and error reduction compared to previous work.

中文翻译：

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差异私有位置跟踪的效用优化综合

差分私有位置轨迹合成 (DPLTS) 最近已成为保护移动用户隐私同时能够分析和共享其位置轨迹的解决方案。DPLTS 的一个关键挑战是最好地保留位置跟踪数据集中的效用，考虑到数据集的高维度、复杂性和异质性，以及效用的不同类型和概念，这并非易事。在本文中，我们介绍了 OptaTrace：一种针对 DPLTS 的实用程序优化且有针对性的方法。给定一个真实的跟踪数据集 D，控制隐私保护强度的差分隐私参数 epsilon，以及感兴趣的效用/误差度量 Err；OptaTrace 使用贝叶斯优化来优化 DPLTS，从而在满足 epsilon 差分隐私的同时最小化输出误差（根据给定的度量 Err 测量）。此外，OptaTrace 引入了一个实用程序模块，其中包含用于实用程序基准测试和选择 Err 的多个内置错误指标，以及用于可访问和交互式 DPLTS 服务的前端 Web 界面。实验表明，与以前的工作相比，OptaTrace 的优化输出可以显着提高效用并减少错误。