As more internet users opt to store their data in cloud storage, ensuring data integrity becomes a paramount concern. The emerging provable data possession (PDP) scheme enables auditors to verify data integrity with reduced bandwidth consumption compared to hash-based alternatives. Nevertheless, most existing PDP variants rely on a centralized node for generating or maintaining user keys, creating a potential single point of failure. Moreover, previous PDP schemes could only detect whether challenged data blocks were corrupted, lacking the ability to pinpoint affected blocks precisely. To tackle these challenges, we propose a novel PDP scheme that eliminates the necessity for a key management center and supports the localization of corrupted data blocks. In our scheme, users no longer need to retain private keys once they cease performing data dynamic operations, thus liberating them from reliance on external entities for key maintenance. Moreover, the new scheme utilizes existing authenticators in the cloud to identify corrupted file blocks, eliminating the necessity of storing hash values for these data blocks as seen in most of existing implementations. This effectively reduces required storage space. Furthermore, we introduce SStore, a decentralized cloud storage platform that incorporates the new PDP scheme to verify data integrity. SStore facilitates public auditing of user data, thereby enhancing transparency in the data verification procedure. Moreover, SStore leverages basic algebraic operations for data auditing, significantly increasing its efficiency. We analyze the security of the new PDP scheme, and evaluate the performance of both the PDP scheme and SStore to demonstrate their efficiency.

Through the intelligent vehicles trip data collection, processing and analysis, so as to predict the vehicles trip destination, this technology can improve the user’s driving experience and the city traffic conditions. The vehicles dispatching system can also judge the real-time road conditions according to the prediction results and plan a more reasonable and efficient driving route, which is of great significance to urban traffic planning and urban construction planning. However, the amount of information in the vehicles data is less, which cannot meet the training needs of some artificial intelligence models. In addition, due to communication technology and other issues, there is a certain degree of deviation between the GPS data of the vehicles and the real data. The previous vehicles destination prediction model did not well eliminate the negative impact of this deviation. In addition, the previous model linearly adds the characteristics of each vehicles’s trip data, which cannot reflect the complex rules of the vehicles’s trip destination. Therefore, to address the above-mentioned drawbacks, we propose a novel vehicle trip destination prediction method named Hybrid Trip Destination Prediction Model of Vehicle Based on Autoencoder and High-Order Interaction Features (HAHIF). The HAHIF model extracts robust hidden features using the autoencoder model and considers the second-order association between them using a factorization machine to improve its superiority and effectiveness. Compared with mainstream benchmarks, the HAHIF model has an MSE value of 0.096, an RMSE value of 0.427, and a MAE value of 0.203 on the public dataset, both of which are the first place, which verifies that the HAHIF model has good predictive ability.

Controller area network (CAN) is the most commonly used bus technology for In-vehicle network and uses multicast communication without corresponding security measures. Therefore, the message data field is vulnerable to tampering and other attacks. Recent machine learning-based intrusion detection methods for CAN bus messages only use the information contained in the message data field and do not take into account the contribution made by the neighboring information of CAN bus messages. In addition, previous models considered the data domain information of CAN bus messages as separate features and did not consider the unique weight of each feature, as well as the second-order interaction information between features. Therefore, we propose a novel intrusion detection model, The Hybrid Similar Neighborhood Robust Factorization Machine Model (HSNRFM), for detecting anomalies in CAN bus messages to address the shortcomings and problems of the previous models. To be able to incorporate the contribution of similar neighborhood information and learn the unique weight parameters of each feature in the model decision process, as well as additional second-order interaction information between features, the HSNRFM model solves the above problem using a similarity calculation method and a factorization machine model. Comprehensive experimental results are compared on real vehicle datasets. The HSNRFM model has AUC values of 0.9216 and 0.901 and AUPR values of 0.9194 and 0.9018 on two real datasets, respectively. And the results show that our proposed HSNRFM model has excellent detection efficiency for intrusion detection of CAN bus messages.