论文标题
差异私有位置轨迹的公用事业优化合成
Utility-Optimized Synthesis of Differentially Private Location Traces
论文作者
论文摘要
差异化位置跟踪综合（DPLT）最近已成为保护移动用户隐私的解决方案，同时启用其位置痕迹的分析和共享。 DPLT中的一个主要挑战是最好地保留位置跟踪数据集中的实用程序，考虑到数据集的高维，复杂性和异质性以及实用程序的各种类型和概念，这是非平凡的。在本文中，我们介绍Optatrace：DPLT的实用性优化和针对性的方法。鉴于真实的跟踪数据集D，差异隐私参数Epsilon控制着隐私保护的强度以及效用/错误度量误差； OptAtrace使用贝叶斯优化来优化DPLT，以便在满足Epsilon-Differential隐私时最小化输出误差（以给定的度量ERR来衡量）。此外，OptAtrace推出了一个实用程序模块，该模块包含几个用于实用程序基准和选择ERR的内置错误指标，以及用于可访问和交互式DPLTS服务的前端Web界面。实验表明，与先前的工作相比，Optatrace的优化输出可以产生大量的实用性改进和误差。
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.
