DPIVE：一种具有个性化隐私级别的区域化位置混淆方案
DPIVE: A Regionalized Location Obfuscation Scheme with Personalized Privacy Levels
摘要：
网络物理系统的普及正在推动基于位置的服务的快速增长。这带来了位置隐私泄露的风险。有效的隐私保护对于各种移动应用程序来说是最重要的。最近，地理不变性和预期的推理误差被提出来限制位置泄漏。在本文中，我们认为，个性化意味着区域化的地理不变性，我们提出了一个区域化的位置混淆机制，称为DPIVE与个性化的效用敏感性。这实质上纠正了Yu等人在NDSS 2017上提出的PIVE框架的差异和失真隐私问题。我们开发DPIVE分为两个阶段。在第一阶段，我们通过划分所有可能的位置来确定不相交的集合，使得同一集合中的不同位置共享保护位置集合（PLS）。在第二阶段，我们构造了一个概率分布矩阵，其中对应于同一PLS的行有自己的效用灵敏度（PLS直径）。此外，通过设计QK-means算法在二维空间中获得更多的搜索空间，我们改进了DPIVE，细化了位置划分，提出了细粒度的个性化，使每个位置都有自己的隐私级别，并赋予定制的隐私预算。两个公共数据集的实验表明，我们的机制具有优越的性能，通常在倾斜的位置。
英文摘要：
The popularity of cyber-physical systems is fueling the rapid growth of location-based services. This poses the risk of location privacy disclosure. Effective privacy preservation is foremost for various mobile applications. Recently, geo-indistinguishability and expected inference error are proposed for limiting location leakages. In this paper, we argue that personalization means regionalization for geo-indistinguishability, and we propose a regionalized location obfuscation mechanism called DPIVE with personalized utility sensitivities. This substantially corrects the differential and distortion privacy problem of PIVE framework proposed by Yu et al. on NDSS 2017. We develop DPIVE with two phases. In Phase I, we determine disjoint sets by partitioning all possible positions such that different locations in the same set share the Protection Location Set (PLS). In Phase II, we construct a probability distribution matrix in which the rows corresponding to the same PLS have their own sensitivity of utility (PLS diameter). Moreover, by designing QK-means algorithm for more search space in 2-D space, we improve DPIVE with refined location partition and present fine-grained personalization, enabling each location to have its own privacy level endowed with a customized privacy budget. Experiments with two public datasets demonstrate that our mechanisms have the superior performance, typically on skewed locations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
