FVC-Dedup: A Secure Report Deduplication Scheme in a Fog-Assisted Vehicular Crowdsensing System
Shunrong Jiang, Jianqing Liu, Yong Zhou, Yuguang Fang
Abstract
It is observed that modern vehicles are becoming more and more powerful in computing, communications, and storage capacity. By interacting with other vehicles or with local infrastructures (i.e., fog) such as road-side units, vehicles and fog devices can collaboratively provide services like crowdsensing in an efficient and secure way. Unfortunately, it is hard to develop a secure and privacy-preserving crowdsensing report deduplication mechanism in such a system. In this article, we propose a scheme FVC-Dedup to address this challenge. Specifically, we develop cryptographic primitives to realize secure task allocation and guarantee the confidentiality of crowdsensing reports. During the report submission, we improve the message-lock encryption (MLE) scheme to realize privacy-preserving report deduplication and resist the fake duplicate attacks. Besides, we construct a novel signature scheme to achieve efficient signature aggregation and record the contributions of each participant fairly without knowing the crowdsensing data. The security analysis and performance evaluation demonstrate that FVC-Dedup can achieve secure and privacy-preserving report deduplication with moderate computing and communication overhead.