The Internet of Vehicles is deployed in an open environment, and protecting its security and data privacy is the challenge. Carrying out the Internet of Vehicles secure authentication before interaction of information is an important part of ensuring the security foundation. Therefore, this article designs a safe and reliable Internet of Vehicles authentication and key agreement schemeassisted by blockchain. This article uses a multi-TA network model to improve the efficiency of authentication. Because of the rapid movement of vehicles, it will continue to appear cross-RSU and TA certification. Considering the disadvantages of most centralised authentication protocols using a single TA, this paper uses the multi-TA model to improve the efficiency of authentication. By usingblockchain technology to store the authentication information of vehicles, the cross-domain authentication of vehicles and the protection of user privacy information can be well realised. At the same time, in order to reduce the time of vehicle authentication, this scheme uses a lightweight calculation operation to complete the whole process of authentication. Through security analysis and results of Proverif simulation, the security of the solution is well proved, our scheme can resist various common attacks. Compared with some existing Internet of Vehicles security authentication protocols, the proposed scheme has a lower cost of computation, communication, and storage.

K-means, as a commonly used clustering method, has been widely applied in data analysis for smart meters. However, this method requires repeatedly computing the similarity between all data points and cluster centers in each iteration, which leads to high computational overhead. Moreover, the process of analyzing electricity consumption data by K-means can cause the leakage of users’ privacy, and the current differential privacy technique adopts a uniform privacy budget allocation for data, which reduces the availability of the data. In order to reduce the computational overhead of smart meter data analysis and improve data availability while protecting data privacy, this paper proposes an adaptive differential privacy-based CK-means clustering scheme, named DPCK. Firstly, we propose a CK-means method by improving K-means, which not only reduces the computation between data and centers but also avoids repeated computation by calculating the adjacent cluster center set and stability region for each cluster, thus effectively reducing the computational overhead of data analysis. Secondly, we design an adaptive differential privacy mechanism to add Laplace noise by calculating a different privacy budget for each cluster, which improves data availability while protecting data privacy. Finally, theoretical analysis demonstrates that DPCK provides differential privacy protection. Experimental results show that, compared to baseline methods, DPCK effectively reduces the computational overhead of data analysis and improves data availability by 11.3% while protecting user privacy.