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With the rapid development of blockchain, many service providers tend to provide users with network resources and services on the basis of blockchain, and most network resources are inseparable from the authorization. However, the current authorization mechanism based on blockchain is not perfect, which is easy to cause the user's identity attributes to be leaked, and not conducive to protecting the privacy of users. In order to improve the authorization mechanism based on the blockchain and protect the user's privacy to the greatest extent during the authorization process, we proposed a privacy-protecting authorization system based on blockchain and zk-SNARK. First of all, SP can authorize users through the blockchain smart contract. Second, in the authorization process, we introduced zk-SNARK. Using the zero-knowledge property of zk-SNARK, the SP can still authorize the users without knowing the identity attribute value of the users. Therefore, the proposed system can better protect the users' attribute privacy. Finally, we analyze the security of the proposed system, and the results show that it is computationally infeasible for an attacker to attack the system.

With the rapid development in smart vehicles, the security and privacy issues of the Vehicular Ad-hoc Network (VANET) have drawn significant attention. Devices in an On-Board Unit (OBU) access to the internet through the Vehicular Communication Module (VCM), hence a real-time and accurate intrusion detection method is favored to be applied in VCM. In this paper, we present a Deep Learning (DL) based end-to-end intrusion detection method to automatically detect malware traffic for OBUs. Different from previous intrusion detection methods, our proposed method only requires raw traffic instead of private information features extracted by the human. The performance is compared with previous methods on a public dataset and a simulated real-life VANET dataset. Experimental results show that our method can attain a higher performance with a lower resources requirement.