Personal health records (PHRs) are located in a patient-centered electronic health system in which users can store and share medical information. However, PHRs have recently been plagued by security issues, such as the leakage of personal health information, illegal access to patient data, and data tampering. Recent security developments, such as introducing an access control policy with attribute-based encryption (ABE) or utilizing blockchain, have only been partially successful in solving these issues. Ongoing challenges to PHR sharing include single points of failure, node cheating attacks, and fair keyword search issues. In this article, we tackle these challenges by introducing a distributed PHR-sharing scheme based on blockchain and ciphertext policy ABE (CP-ABE), which allows for fast and efficient encryption and decryption. Blockchain maintains the integrity and the tracing source of the data while also recording all operations on the data in the form of transactions. In addition, the blockchain nodes act as attribute authorities to construct the CP-ABE cryptosystem. The tracing of malicious blockchain nodes is realized by tracing cryptography algorithms. Furthermore, the fair retrieval of ciphertext is achieved by employing smart contracts. To overcome the limited storage capacity of blockchain, we adopt both the on-chain and off-chain storage modes in our new system. Security analysis indicates that our new scheme remains intact when threatened by an indistinguishable chosen plaintext attack (IND-CPA) and an indistinguishable chosen keywords attack (IND-CKA). As such, we conclude that our proposed approach is feasible and efficient.

The vehicular social network (VSN) is an emerging mobile communication system combining a vehicle ad hoc network (VANET) with a social network. It provides a new means of sharing, disseminating, and delivering data for passengers, drivers, and vehicles. However, a VSN may expose users’ private information, such as identities, location information, and trajectories, and tampering with shared data may lead to security and safety problems in vehicle systems. Considering the security and privacy preservation of shared data, we propose a lightweight decentralized multiauthority access control scheme based on ciphertext-policy attribute-based encryption (CP-ABE) and blockchain, by which a decentralized multiauthorization node supports vehicle users by performing lightweight calculations with the assistance of the vehicle cloud service provider (VCSP). We use blockchain to record storage and access transactions, achieving self-verification by users and tamper-resistance of ciphertexts. An improved smart contract reduces the workload of verification by users and achieves privacy preservation by hiding the policy. It supports user revocation and outsourced decryption, enabling more flexibility and better performance. A security and performance analysis shows that our scheme has clear advantages over existing schemes.

As an important component of the Industrial Internet of Things (IIoT), the smart factory uses IIoT and equipment-monitoring technology to collect data to reasonably arrange the production. A large number of data is collected and uploaded to the IIoT cloud platform. However, the IIoT cloud platform is semitrusted and has structural limitations and vulnerability, which makes it necessary to realize data dynamic security sharing and malicious users' tracking. In this article, we show that the most recent work on this issue is still vulnerable to security threats at first. Then, a blockchain-enabled dynamic and traceable data-sharing scheme for a smart factory is proposed. Blockchain performs the user authentication and stores the ciphertext index and public keys to avoid tampering with shared data. The tracking algorithm tracks malicious users and adds them to a revocation list embedded in the ciphertext. And the authority can flexibly select domain or user revocation as required. The LSSS access policy is hidden to protect user privacy, and the cloud server uses the match test algorithm to detect whether users meet the hidden access policy. Additionally, online–offline encryption and outsourced decryption improve the efficiency of the scheme where the ciphertext and the pairing operations required for decryption achieve constant size. A performance analysis shows that the scheme can resist a variety of collusion attacks, and simulations show that it outperforms current schemes.

Aspect-level sentiment analysis plays a pivotal role in fine-grained sentiment categorization, especially given the rapid expansion of online information. Traditional methods often struggle with accurately determining sentiment polarity when faced with implicit or ambiguous data, leading to limited accuracy and context-awareness. To address these challenges, we propose the Deep Context-Aware Sentiment Analysis Model (DCASAM). This model integrates the capabilities of Deep Bidirectional Long Short-Term Memory Network (DBiLSTM) and Densely Connected Graph Convolutional Network (DGCN), enhancing the ability to capture long-distance dependencies and subtle contextual variations.The DBiLSTM component effectively captures sequential dependencies, while the DGCN component leverages densely connected structures to model intricate relationships within the data. This combination allows DCASAM to maintain a high level of contextual understanding and sentiment detection accuracy.Experimental evaluations on well-known public datasets, including Restaurant14, Laptop14, and Twitter, demonstrate the superior performance of DCASAM over existing models. Our model achieves an average improvement in accuracy by 1.07% and F1 score by 1.68%, showcasing its robustness and efficacy in handling complex sentiment analysis tasks.These results highlight the potential of DCASAM for real-world applications, offering a solid foundation for future research in aspect-level sentiment analysis. By providing a more nuanced understanding of sentiment, our model contributes significantly to the advancement of fine-grained sentiment analysis techniques.

The Internet of Things has been applied in various fields of industry, which has promoted the intelligent development of the industry and improved efficiency in industrial production. The devices involved in the IoT have generated useful and sensitive data over time and upload the data to the cloud to realize real-time data sharing. To ensure the confidentiality of data, many systems use attribute-based encryption primitive to encrypt data. However, there are still some security and privacy problems in this mode, such as the lack of identification of malicious users who leaked private keys, performance bottleneck caused by excessive reliance on a single central authority (CA), and vulnerability because a single CA holds the private keys of all users in the system. In this article, white-box tracking is used to identify malicious users. The alliance chain is introduced to support multiauthority environments, where the consensus nodes are managed by different authorities and assist CA in generating partial private keys. To protect users’ privacy, users remain anonymous at all times during their interactions with blockchain consensus. The security analysis and simulation results show that the proposed scheme outperformed other comparable schemes, indicating that it is a preferable scheme.