In vehicular ad-hoc networks (VANETs), certificate revocation is a critical technique to synchronize the invalid vehicle information accurately and timely across the network, especially in scenarios involving multiple edge domains. In order to achieve efficient and privacy-preserving certificate revocation list (CRL) synchronization, we propose ESGA, an efficient and secure geo-aware certificate revocation scheme for blockchain-based multi-domain VANETs. Since invalid vehicles are driving within specific edge domains during certain time periods, we design a hierarchical Geo-CRL structure comprising a blockchain-based global CRL and geographically distributed local CRLs. Furthermore, we design an adaptive geo-aware synchronization mechanism which enables timely local CRLs update, while ensuring periodic or event-driven updates of the global CRL on the blockchain. Finally, to protect the CRL information from pseudonym linking attacks, we design linkage values chain-based certificate revocation method for privacy preservation. The experimental results demonstrate that ESGA can significantly improve CRL synchronization efficiency with lower CRL query latency and storage overhead, and to protect the privacy of certificate revocation in blockchain-based multi-domain VANETs.

Spatial crowdsourcing is widely used in various applications in vehicular ad hoc networks (VANETs), such as navigation system, traffic control, and event reporting. However, exposing and disclosing the spatial-temporal features of vehicles in the crowdsourcing services will definitely raise serious privacy issues. Existing unified privacy-preserving strategy for vehicles will cause excessive or insufficient preservation, thus result in relatively low-quality services. To solve these problems, we propose personalized privacy preserving for spatial crowdsourcing by reinforcement learning in VANETs. First, we propose a multifactor-based personalized privacy-preserving model to adjust the vehicles’ privacy-preserving level in the scenario of spatial crowdsourcing. And, we employ reinforcement learning to dynamically adjust the model in different situations. Furthermore, we propose an optimal local differential privacy mechanism to maintain the optimal tradeoff between data privacy and data utility, which can achieve personalized privacy preserving in the task allocation. We conduct extensive simulations with the real-world traffic trajectory dataset T-drive, and use the Q-learning algorithm to dynamically adjust the model. The experiments demonstrate that our scheme can enhance data utility by 78.5% with personalized privacy settings.

With the rapid expansion of vehicular ad-hoc networks (VANETs), the dynamic traffic environment raises concerns regarding accurate and secure data sharing. Unauthorized entities may exploit vulnerabilities to access sensitive information within shared data. To address these challenges, we propose DRL-APG, a deep reinforcement learning based adaptive policy generation scheme, to enable smarter security policies that can better handle complex changes in data sharing among vehicles. DRL-APG adopts hybrid modeling to capture environment states and fine-grained policy optimization using an ARIMA (Autoregressive Integrated Moving Average)-based reward mechanism to generate adaptive policies. Extensive simulations demonstrate DRL-APG outperforms existing schemes in different kinds of VANET situations including peak/off-peak hours and varying speed limits. In traffic congestion scenario across three simulation typical road networks, average accident zone speed increases 21.7%, 25.6% and 27.8% respectively after data sharing with the policy generated by our proposed DRL-APG, compared to existing schemes.