Jiang Nanlan, Zhai Yinan, Wang Yujun, Yin Xuesong, Yang Sai, Xu Pingping
School of Communication and Artificial Intelligence, School of Integrated Circuits, Nanjing Institute of Technology, Nanjing 211167, China.
Electrical and Computer Engineering Faculty, Brown University, Providence, RI 02903, USA.
Sensors (Basel). 2024 Sep 23;24(18):6153. doi: 10.3390/s24186153.
With the development of the Internet of Things (IoT) and edge computing, more and more devices, such as sensor nodes and intelligent automated guided vehicles (AGVs), can serve as edge devices to provide Location-Based Services (LBS) through the IoT. As the number of applications increases, there is an abundance of sensitive information in the communication process, pushing the focus of privacy protection towards the communication process and edge devices. The challenge lies in the fact that most traditional location privacy protection algorithms are not suited for the IoT with edge computing, as they primarily focus on the security of remote servers. To enhance the capability of location privacy protection, this paper proposes a novel K-anonymity algorithm based on clustering. This novel algorithm incorporates a scheme that flexibly combines real and virtual locations based on the requirements of applications. Simulation results demonstrate that the proposed algorithm significantly improves location privacy protection for the IoT with edge computing. When compared to traditional K-anonymity algorithms, the proposed algorithm further enhances the security of location privacy by expanding the potential region in which the real node may be located, thereby limiting the effectiveness of "narrow-region" attacks.
随着物联网（IoT）和边缘计算的发展，越来越多的设备，如传感器节点和智能自动导引车（AGV），可以作为边缘设备，通过物联网提供基于位置的服务（LBS）。随着应用数量的增加，通信过程中存在大量敏感信息，使得隐私保护的重点转向通信过程和边缘设备。挑战在于，大多数传统的位置隐私保护算法并不适用于具有边缘计算的物联网，因为它们主要关注远程服务器的安全性。为了增强位置隐私保护能力，本文提出了一种基于聚类的新型K匿名算法。这种新算法包含一种根据应用需求灵活组合真实位置和虚拟位置的方案。仿真结果表明，所提出的算法显著提高了具有边缘计算的物联网的位置隐私保护。与传统的K匿名算法相比，该算法通过扩大真实节点可能所在的潜在区域，进一步增强了位置隐私的安全性，从而限制了“窄区域”攻击的有效性。
