Ciphertext-Policy Attribute-Based Encryption (CP-ABE) schemes with user revocation allow for dynamic updates to users' access rights. However, existing schemes often encounter issues such as low re-encryption efficiency, inflexible access control, and susceptibility to collusion attacks. To address these challenges, we propose UR-CP-ABE, an efficient user revocation scheme built upon a double encryption method. Specifically, UR-CP-ABE stores both valid and revoked users' identity information in a binary tree. This design enables flexible user authority revocation by modifying only the binary tree's relevant secret sub-items. Moreover, the scheme limits the scope of revocation-induced binary tree updates to a single sub-item. This key optimization resolves the critical issue where re-encryption overhead scales linearly with the number of attributes of revoked users. In addition, we eliminate the possibility of attackers constructing secret sub-keys, preventing the collusion attacks. UR-CP-ABE also supports bidirectional revocation, allowing for the revocation and restoration of user rights, which is not available in other related schemes. Our theoretical analysis and experiments demonstrate that UR-CP-ABE has better performance than other schemes, especially in the re-encryption stages. Moreover, UR-CP-ABE has been proved to be secure based on the decisionalq-parallel BDHEhardness assumption in the standard model.

With the growing demand for public data openness, sharing data while preserving data privacy has become a critical challenge. Traditional techniques, such as data anonymization and differential privacy, provide baseline privacy guarantees but face some limitations, including limited generalizability and utility degradation due to inappropriate perturbations. To overcome these limitations, this paper proposes a hybrid diffusion model for generating privacy-preserving tabular data. Unlike single-structure data generation models, our proposed approach integrates differential privacy with two lightweight generative models to effectively balance data privacy and data utility. Specifically, our approach consists of three phases: data preprocessing, privacy protection, and data generation. In the data preprocessing phase, the adaptive techniques are used for data normalization. During the privacy protection phase, Gaussian noise and randomized response mechanisms are applied to enhance data privacy. Finally, in the data generation phase, Gaussian diffusion is used for numerical attributes and multinomial diffusion for categorical attributes, which effectively handles the original data of mixed types. This design enhances both the stability of the generative model and the diversity of the synthetic data. Experiments on six public datasets demonstrate that although our approach incurs only a slight reduction in machine learning utility, measured by classification accuracy, F1 score, and regression R2scores, it greatly improves privacy metrics compared to the state-of-the-art tabular diffusion models.

While 5G can facilitate high-speed Internet access and make over-the-horizon control a reality for unmanned aerial vehicles (UAVs; also known as drones), there are also potential security and privacy considerations, for example, authentication among drones. Centralized authentication approaches not only suffer from a single point of failure but they are also incapable of cross-domain authentication. This complicates the cooperation of drones from different domains. To address these limitations, a blockchain-based cross-domain authentication scheme for intelligent 5G-enabled Internet of drones is proposed in this article. Our approach employs multiple signatures based on threshold sharing to build an identity federation for collaborative domains. This allows us to support domain joining and exiting. Reliable communication between cross-domain devices is achieved by utilizing smart contract for authentication. The session keys are negotiated to secure subsequent communication between two parties. Our security and performance evaluations show that the proposed scheme is resistant to common attacks targeting Internet of Things (IoT) devices (including drones), as well as demonstrating its effectiveness and efficiency.

Motivated by Industry 4.0, 5G-enabled unmanned aerial vehicles (UAVs; also known as drones) are widely applied in various industries. However, the open nature of 5G networks threatens the safe sharing of data. In particular, privacy leakage can lead to serious losses for users. As a new machine learning paradigm, federated learning (FL) avoids privacy leakage by allowing data models to be shared instead of raw data. Unfortunately, the traditional FL framework is strongly dependent on a centralized aggregation server, which will cause the system to crash if the server is compromised. Unauthorized participants may launch poisoning attacks, thereby reducing the usability of models. In addition, communication barriers hinder collaboration among a large number of cross-domain devices for learning. To address the abovementioned issues, a blockchain-empowered decentralized horizontal FL framework is proposed. The authentication of cross-domain UAVs is accomplished through multisignature smart contracts. Global model updates are computed by using these smart contracts instead of a centralized server. Extensive experimental results show that the proposed scheme achieves high efficiency of cross-domain authentication and good accuracy.

The Internet of Health Things (IoHT) is a medical concept that describes uniquely identifiable devices connected to the Internet that can communicate with each other. As one of the most important components of smart health monitoring and improvement systems, the IoHT presents numerous challenges, among which cybersecurity is a priority. As a well-received security solution to achieve fine-grained access control, ciphertext-policy weighted attribute-based encryption (CP-WABE) has the potential to ensure data security in the IoHT. However, many issues remain, such as inflexibility, poor computational capability, and insufficient storage efficiency in attributes comparison. To address these issues, we propose a novel access policy expression method using 0-1 coding technology. Based on this method, a flexible and efficient CP-WABE is constructed for the IoHT. Our scheme supports not only weighted attributes but also any form of comparison of weighted attributes. Furthermore, we use offline/online encryption and outsourced decryption technology to ensure that the scheme can run on an inefficient IoT terminal. Both theoretical and experimental analyses show that our scheme is more efficient and feasible than other schemes. Moreover, security analysis indicates that our scheme achieves security against a chosen-plaintext attack.

The classical rough set theory has been extended to fuzzy data environments by many authors, resulting in the development of the fuzzy rough set. Recently, vague set is treated as an extension of fuzzy set, but the existing theories and approaches of fuzzy rough set could not be applied directly to data set represented by vague set. In this article, we attempt to establish a theoretical model for vague data by combing both rough set and vague set. We first introduced the basic notions of vague t-norms and t-conorms. Next, we developed a general vague rough approximation set for generalizing to fuzzy rough set. Then, to overcome the inconvenience of dealing vague data, we also introduced a model for transforming a vague set into a fuzzy set to take the place of an interval vague set. Moreover, we also give some perspectives for future research.