A Personalized Location Privacy Protection System in Mobile Crowdsourcing

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 comprehensive evaluation of the proposed location privacy protection system using real data sets, and the results demonstrate that the system can effectively balance the location privacy protection of crowd workers and the availability of location data, thereby improving the efficiency and reliability of MCS.