Aiming to address issues of query request submission, data transmission leakage, and vehicle privacy leakage caused by untrustworthy cooperating Partners (CPs) in privacy-preserving caching for Internet of Vehicles (IoV), this paper proposes a trust mechanism privacy protection scheme combining blockchain and multi-party evaluation (TMPP-BMPE). First, a data broadcasting mechanism is proposed based on the Paillier encryption algorithm and the Elliptic Curve Digital Signature Algorithm (ECDSA) for data protection. The homomorphic encryption algorithm and the ECDSA are applied for data privacy protection during transmission. Second, a trust mechanism based on multi-party assessment is proposed. The trustworthiness of CPs is comprehensively assessed considering assessment indicators from multiple entities, mitigating risks of interacting with untrustworthy CPs. Finally, a blockchain-assisted trust management scheme is designed to effectively prevent malicious tampering with trusted data. The simulation experiment results show that the TMPP-BMPE performs well in protecting data privacy, evaluating the trustworthiness of CPs, and preventing data tampering. It provides valuable insights for security and trust establishment in IoV.

Location-based services make life easier, but they also involve privacy leakage issues. Many location privacy protection algorithms have been proposed to protect the privacy of users. However, these algorithms are usually based on theoretical data, and there are no actual user data to support studies of location privacy protection. To address this problem, we introduce a credit value, convert credit data from users into credit values using the multiple-attribute decision making (MADM) algorithm, store the credit values and transaction information from the anonymous zone construction process in conjunction with a blockchain, and propose a credit value reward and punishment mechanism that treats anonymous zone construction as a two-party game between the requestor and participant. In this game, a credit value reward and punishment mechanism is used to constrain undesirable behaviors. Through simulation experiments, it is verified that the method can be applied in practical scenarios, effectively constrain undesirable user behaviors, quickly construct anonymous zones, and reduce the probability of user location leakage issues.

The rapid progress of the Internet has exposed networks to an increased number of threats. Intrusion detection technology can effectively protect network security against malicious attacks. In this paper, we propose a ReliefF-P-Naive Bayes and softmax regression (RP-NBSR) model based on machine learning for network attack detection to improve the false detection rate and F1 score of unknown intrusion behavior. In the proposed model, the Pearson correlation coefficient is introduced to compensate for deficiencies in correlation analysis between features by the ReliefF feature selection algorithm, and a ReliefF-Pearson correlation coefficient (ReliefF-P) algorithm is proposed. Then, the Relief-P algorithm is used to preprocess the UNSW-NB15 dataset to remove irrelevant features and obtain a new feature subset. Finally, naïve Bayes and softmax regression (NBSR) classifier is constructed by cascading the naïve Bayes classifier and softmax regression classifier, and an attack detection model based on RP-NBSR is established. The experimental results on the UNSW-NB15 dataset show that the attack detection model based on RP-NBSR has a lower false detection rate and higher F1 score than other detection models.