A Personalized Location Privacy Protection System in Mobile Crowdsourcing

作者：Chenghao Zhang, Yingjie Wang, Weilong Wang, Haijing Zhang, Zhaowei Liu, Xiangrong Tong, Zhipeng Cai · 发表于：IEEE Internet of Things Journal · 年份：2023 · DOI：10.1109/jiot.2023.3325368 · 被引用次数：29 · 研究领域：Privacy-Preserving Technologies in Data、Mobile Crowdsensing and Crowdsourcing、Privacy, Security, and Data Protection

With the rapid progression of mobile crowdsourcing (MCS) technology, its growing influence in our daily lives has established it as a crucial component of modern society. However, while the convenience of MCS is widely appreciated, it also poses significant threats to personal privacy, particularly location privacy. This article introduces a novel system for personalized location privacy protection in MCS. The system is divided into three main parts. The first part presents an innovative algorithm that calculates the location privacy level of crowd workers. This algorithm is crucial in determining the location privacy level required by each individual crowd worker. The second part involves the design of a personalized differential privacy protection (P-DP) algorithm, which is based on the exponential mechanism. This algorithm provides varying degrees of privacy protection strength, tailored to the location privacy protection level of each crowd worker. Furthermore, we incorporate a trusted third party (TP) server to act as an intermediary. This server eliminates any correlation between the crowd workers and the data. It is also tasked with calculating the location privacy level and reward for each crowd worker. The third part of the system is the personalized localized differential privacy (LDP) protection (P- LDP) algorithm, this algorithm is designed to further solve the problem of privacy disclosure caused by the TP server being attacked. Finally, we have conducted a compr...