ELPPS: An Enhanced Location Privacy Preserving Scheme in Mobile Crowd-Sensing Network Based on Edge Computing

Abstract

Mobile Crowd-Sensing (MCS) is gradually extended to the edge network to reduce the delay of data transmission and improve the ability of data processing. However, a challenge is that there are still loopholes in the protection of privacy data, especially in location-based services. The attacker can reconstruct the location relationship network among the correlation about the environment information, identity information, and other sensing data provided by mobile users. Moreover, in the edge environment, this kind of attack is more accurate and more threatening to the location privacy information. To solve this problem, we propose a location privacy protection scheme (ELPPS) for a mobile crowd-sensing network in the edge environment, to protect the position correlation weight between sensing users through differential privacy. We use the grid anonymous algorithm to confuse the location information in order to reduce the computing cost of edge nodes. The experiment results show that the proposed framework can effectively protect the location information of the sensing users without reducing the availability of the sensing task results, and has a low delay.