An Approach Based on Customized Robust Cloaked Region for Geographic Location Information Privacy Protection

Author:

Shen Zhidong12ORCID,Lu Siyuan2,Huang Huijuan2,Yuan Meng2,Tang Guoming3,Chen Weiying1,Zhang Taige1,Zhong Ting1

Affiliation:

1. Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430079, China

2. International School of Software, Wuhan University, Wuhan 430079, China

3. Science & Technology on Information Systems Engineering Lab, National University of Defense Technology, Changsha, China

Abstract

Location-based services (LBS) have gained huge popularity because of the easy availability of modern mobile devices and the fast development of geographical information science (GIS). However, the lack of protection for private user positions might give rise to privacy concerns. This kind of problem is especially serious in mobile application environment because many mobile applications tend to use LBS. In this paper, we propose a new privacy preserving approach using customized robust cloaked region (RCR), depending on a peer-to-peer structure and the premise that users do not trust each other when sharing their geographical locations. Two algorithms are used to generate the RCR with high user density. The area of the RCR is controlled by the user’s demanded degree of protection. To enhance the resistance to regional background knowledge attack, we incorporate a location semantic value into each unit of the user map. According to extensive simulations, our method can effectively obfuscate a user’s geographical location into a highly indistinguishable region because of the disturbance of nearby users and different equally possible locations.

Funder

National Key R&D Program of China

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Computer Science Applications

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Towards Privacy-Preserving Spatial Distribution Crowdsensing: A Game Theoretic Approach;IEEE Transactions on Information Forensics and Security;2022

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