Enabling Reliable and Anonymous Data Collection for Fog-Assisted Mobile Crowdsensing With Malicious User Detection

Mingyang Song, Zhongyun Hua, Yifeng Zheng, Rushi Lan, Qing Liao, Guoai Xu

Abstract: The rapid developments of mobile devices and fog computing have facilitated the data collection paradigm of fog-assisted mobile crowdsensing, providing great convenience for individuals with limited resources to collect large-scale data. However, the openness of crowdsensing network and the untrusted behaviors of some task participants raise concerns regarding participants’ privacy and data reliability. Previous works mostly focus on preserving the privacy of task participants and often overlook the issue of data reliability in the presence of dishonest participants. In this paper, we propose a new data collection scheme tailored for fog-assisted mobile crowdsensing. It enables the cloud to detect invalid sensing data in the ciphertext domain, simultaneously ensuring data confidentiality and reliability. Additionally, our scheme is designed to protect the anonymity of honest task participants while guaranteeing the traceability of dishonest participant once invalid data are detected. Formal analysis is provided to prove the correctness and security of our scheme. Furthermore, we implement our scheme to evaluate its performance, and the experimental results demonstrate that it can achieve the aforementioned security properties with modest performance overhead.

External IDs:dblp:journals/tmc/SongHZLLX26