A DAG-Based Reputation Mechanism for Preventing Peer Disclosure in SIoV

Abstract

The sensitive information of vehicles which is closely related to the safety of transportation makes the privacy problems in the vehicular networks a popular concern. The development of artificial intelligence (AI) has led the Internet of Vehicles (IoV) to the next phase of intelligence, the Social IoV (SIoV). For social purposes, vehicles may upload captured images with more sensitive information. As a result, the privacy problem is even more serious in SIoV. The exited studies of privacy-preserving methods in vehicular networks mainly consider the spontaneous privacy disclosure. However, the main privacy leakage in real life comes from peer disclosure rather than spontaneous privacy disclosure, which is usually ignored. Therefore, this article innovatively presents a decentralized scheme for solving the peer disclosure issues in SIoV, which, to the best of our knowledge, is the first research in SIoV peer disclosure discussion. A directed acyclic graph (DAG)-based mutual supervision (Dmsv) algorithm is designed for mutually distrustful vehicles. It is verified that our proposed algorithm reduces at least 72% multidimentional privacy loss of image entropy leakage probability when compared with nonpeer disclosure prevention. The decentralized scheme also achieves a relatively low delay of around 24 s from the transaction generation to a network-wide consensus. This article provides a feasibility of applying DAG in a mobile system and provides guidance on how the transportation situation influences the confirmation delay and how to adjust the incentive mechanism according to the transportation situation to maintain robustness.