With the rapid development of physical networks, tens of billions of Internet of Things (IoT) devices have been deployed worldwide. Access control is essential in the IoT system, which manages user access to vital IoT data. However, access control for the IoT is mainly based on centralized trusted servers, which face problems such as a single point of failure and data leakage. To tackle these challenges, we propose an access control framework for the IoT by combining blockchain and Intel software guard extension (SGX) technology. A blockchain validates both IoT devices and edge servers added to the network. The access control contract is deployed on the blockchain, which can manage attribute-based access control policies in a fine-grained manner and make access control decisions flexibly. SGX technology is introduced into the edge computing server to realize the confidentiality of data processing. Finally, we implemented the prototype of the framework on Quorum and conducted extensive experiments and theoretical analyses on the performance of the blockchain. The results of the experimental tests and theoretical analyses show that our framework has more advantages in computing costs and on-chain storage costs.

The massive data generated by the Internet of Things (IoT) is often outsourced to the cloud, leading to a separation between data ownership and management. Access control during the data’s validity period and assured deletion once that period expires are both crucial for protecting privacy. While recent research has primarily focused on access control, assured deletion has received less attention. Existing assured deletion schemes can be classified into key-control based and cryptographic policy based methods, but to varying degrees, they have limitations such as requiring a trusted third party, high encryption overhead, lack of support for deletion verification and fine-grained access control. To address these limitations, we propose BBAD, a blockchain-based assured deletion scheme that leverages smart contracts for fine-grained access control, employs Shamir secret sharing and re-encryption for assured key deletion, and utilizes Merkle Hash Tree (MHT) for public deletion verification. Notably, BBAD eliminates the need for a trusted third party, exhibits low computational overhead, supports customizable deletion time limit, and enables offline verification of deletion for users. Our experimental comparison with two prominent alternatives, Secure Electronic-Document Self-Destructing with Identity-Based Timed-Release Encryption (ESITE) and Key-Policy Attribute-Based Encryption for Assured Deletion (AD-KP-ABE), demonstrates that BBAD reduces data processing time by over 46.5%, data deletion time by 98.4%, and deletion verification time by 99.0%.

Cloud computing is a promising computing paradigm which has drawn extensive attention. Serious concerns over the reliability and confidentiality of the outsourced data are arising. Traditional encryption methods can, guarantee data confidentiality, however, it also limits the cloud's functionality as few operations are supported over encrypted data. In this study, the authors construct an enhanced cloud that not only provides secure and robust data storage, but also supports the functionality that the cipher data can be forwarded without being retrieved back. Specifically, they design an all‐or‐nothing‐transform‐based encryption and a variant of ElGamal‐based proxy re‐encryption algorithms, blending them with Reed–Solomon code, the authors’ scheme is quite more efficient compared with previous studies because it only needs to update partial data blocks instead of the whole file for data forwarding. Besides, the authors’ scheme also satisfies another practical property that the original data owner can no longer decrypt or forward the re‐encrypted data to others after a complete forwarding instance, which is termed to be ‘original inaccessibility’ in this study. Analysis shows that the authors’ scheme is secure and satisfactory. Finally, the authors theoretically and experimentally evaluate its performance and the results indicate that their scheme is efficient during file dispersal, forward and retrieval.

Navigation resource allocation for low-earth-orbit (LEO) constellations refers to the optimal allocation of navigational assets when the number and allocation of satellites in the LEO constellation have been determined. LEO constellations can not only transmit navigation enhancement signals but also enable space-based monitoring (SBM) for real-time assessment of GNSS signal quality. However, proximity in the frequencies of LEO navigation signals and SBM can lead to significant interference, necessitating isolated transmission and reception. This separation requires that SBM and navigation signal transmission be carried out by different satellites within the constellation, thus demanding a strategic allocation of satellite resources. Given the vast number of satellites and their rapid movement, the visibility among LEO, medium-earth-orbit (MEO), and geostationary orbit (GEO) satellites is highly dynamic, presenting substantial challenges in resource allocation due to the computational intensity involved. Therefore, this paper proposes an optimal allocation algorithm for LEO constellation navigation resources based on dynamic programming. In this algorithm, a network model for the allocation of navigation resources in LEO constellations is initially established. Under the constraints of visibility time windows and onboard transmission and reception isolation, the objective is set to minimize the number of LEO satellites used while achieving effective navigation signal transmission and SBM. The constraints of resource allocation and the mathematical expression of the optimization objective are derived. A dynamic programming approach is then employed to determine the optimal resource allocation scheme. Analytical results demonstrate that compared to Greedy and Divide-and-Conquer algorithms, this algorithm achieves the highest resource utilization rate and the lowest computational complexity, making it highly valuable for future resource allocation in LEO constellations.

With the rapid development of the Internet, e-commerce and social media have continued to develop and grow. Merchants tend to better understand users’ attitudes and emotional tendencies through comments. As the number of user reviews increases, the previous sentiment analysis methods have highlighted the problems of high cost and high error rate. How to use more advanced methods to analyse comments has become an urgent problem to be solved. In order to solve the above problems, in this article, we propose a fine-grained sentiment analysis method based on dependency tree and graph neural network, which can help businesses and social network platforms to identify users’ sentiment tendencies and can be subsequently used in recommendation systems and public opinion analysis systems. The experimental results show that our scheme has achieved the best results.