Local Differential Privacy for correlated location data release in ITS

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Kah Meng Chong , Amizah Malip
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引用次数: 0

Abstract

The ubiquity of location positioning devices has facilitated the implementation of various Intelligent Transportation System (ITS) applications that generate an enormous volume of location data. Recently, Local Differential Privacy (LDP) has been proposed as a rigorous privacy framework that permits the continuous release of aggregate location statistics without relying on a trusted data curator. However, the conventional LDP was built upon the assumption of independent data, which may not be suitable for inherently correlated location data. This paper investigates the quantification of potential privacy leakage in a correlated location data release scenario under a local setting, which has not been addressed in the literature. Our analysis shows that the privacy guarantee of LDP could be degraded in the presence of spatial–temporal and user correlations, albeit the perturbation is performed locally and independently by the users. This privacy guarantee is bounded by a privacy barrier that is affected by the intensity of correlations. We derive several important closed-form expressions and design efficient algorithms to compute such privacy leakage in a correlated location data. We subsequently propose a Δ-CLDP model that enhances the conventional LDP by incorporating the data correlations, and design a generic LDP data release framework that renders adaptive personalization of privacy preservation. Extensive theoretical analyses and simulations on scalable real datasets validate the security and performance efficiency of our work.
在智能交通系统中发布相关位置数据的本地差分保密功能
位置定位设备的普及促进了各种智能交通系统(ITS)应用的实施,这些应用产生了大量的位置数据。最近,有人提出了本地差分隐私(LDP)作为一种严格的隐私框架,允许在不依赖可信数据管理者的情况下持续发布总体位置统计数据。然而,传统的 LDP 是建立在独立数据假设的基础上的,这可能不适合内在相关的位置数据。本文研究了在本地设置下相关位置数据发布场景中潜在隐私泄露的量化问题,而这一问题在文献中尚未涉及。我们的分析表明,在存在时空相关性和用户相关性的情况下,尽管扰动是在本地由用户独立执行的,但 LDP 的隐私保证可能会降低。这种隐私保证受隐私屏障的约束,而隐私屏障受相关性强度的影响。我们推导出几个重要的闭式表达式,并设计出高效算法来计算相关位置数据中的隐私泄漏。随后,我们提出了一个 Δ-CLDP 模型,通过纳入数据相关性来增强传统的 LDP,并设计了一个通用的 LDP 数据发布框架,以实现隐私保护的自适应个性化。在可扩展的真实数据集上进行的大量理论分析和模拟验证了我们工作的安全性和性能效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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