Abstract Smart grid systems use communication infrastructure and embedded devices to achieve data communication between devices and control centers. Ensuring the secure transmission and privacy protection of user electricity consumption data is a core challenge facing smart grids. However, existing authentication schemes often suffer from centralized bottlenecks, limited scalability, and insufficient privacy protection. This paper proposes a Multi-Authority Privacy-Preserving Aggregate Signature scheme (MAPS-G) specifically for smart grid environments. A distributed multi-authorization architecture is used to eliminate single points of failure, and the identity of devices is protected by an anonymous mechanism. To further reduce communication and computational overheads, an aggregator is introduced to batch verify messages using the aggregate signature technique. At the same time, a pseudonym update strategy is designed to ensure the unlinkability between different sessions. Security analysis shows that the proposed scheme is robust in terms of privacy protection and resistance to common attacks. Performance analysis experiments show that the proposed scheme significantly outperforms existing models in terms of computation, communication, and energy efficiency, and is suitable for resource-constrained devices such as smart meters.

Existing online banking identity authentication protocols typically rely on centralized authorization centers or trusted third parties, which can lead to single points of failure, key escrow risks, and privacy leaks. Addressing multi-party collaboration scenarios under a trustless assumption, this paper proposes an Overseen-by-Multiple-Authorities Identity Authentication (OBIA) scheme tailored for electronic banking environments. This solution integrates attribute-based cryptography with distributed key generation (DKG) mechanisms across multiple authorization centers. Users derive complete personal keys from attribute-based private keys embedded with random factors, enabling implicit binding of identity and attributes. To support dynamic attribute changes and permission revocation, a hierarchical time-driven key update mechanism is designed. Combined with non-interactive zero-knowledge proofs (NIZK) and elliptic curve cryptography (ECC), this enables efficient, privacy-preserving authentication. At the data storage layer, an optimized multi-layer Merkle hash tree (MMHT) structure reduces blockchain storage and verification overhead. Security analysis demonstrates that the proposed scheme effectively resists forgery, replay, man-in-the-middle, and key compromise attacks. Experimental results show that compared to existing multi-authorization authentication schemes, this approach exhibits superior or comparable computational and communication overhead while significantly enhancing the system’s decentralization and auditability.

Designing a functional occlusal surface for denture crowns is a complex and important task in prosthodontics. Manual design is time-consuming and heavily relies on the dentist's experience, as it requires careful consideration of occlusal function. Due to the limitations of manual design, the field has turned to data-driven methods for occlusal surface design. However, many of these methods neglect critical geometric details, such as normals and curvature, impacting the quality of the occlusal surface. In this paper, we introduce Diff-OSGN, a novel denture crown occlusal surface generation network based on a denoising diffusion model, which focuses on generating the detailed geometric structure of denture crowns. We model the occlusal surface as a geometry map based on the occlusal plane, incorporating height and normal maps rasterized from intra-oral crown scanning. Both maps represent occlusal surface geometry, and their combination further enhances these details. Considering the crucial occlusal information, we extract features from the geometry maps of adjacent and occlusal teeth, using them as conditions in the reverse diffusion process to train our network for optimal occlusal function. Additionally, we define three geometric operators and corresponding loss functions as constraints to better extract geometric features of the target occlusal surface, such as ridges and grooves, for adequate supervision. Our results demonstrate that Diff-OSGN provides quantitatively and qualitatively superior performance than competing baselines and state-of-the-art methods.

The vast number of IoT devices is distributed across multiple trust domains, each with distinct security policies, trust models, and permission management methods. This diversity increases the risk of privacy exposure during cross-domain communications. At the same time, traditional authentication methods have problems, such as complex certificate management, high risk of key escrow, and reliance on trusted third parties. To address the above problems, this article proposes a novel method, enhancing device privacy through certificateless encryption and zero-knowledge proof (DPCZK). DPCZK achieves decentralization by leveraging a consortium blockchain as a trust bridge across different domains. The adoption of certificateless encryption mitigates the incomplete trust issues associated with the key generation center. Furthermore, DPCZK incorporates an identity-hiding mechanism based on zero-knowledge proof, enabling devices to authenticate and interact with resources anonymously during cross-domain operations, thereby safeguarding their privacy. Additionally, through threshold technology, the target domain can reveal the true identities of malicious devices and revoke their access rights, ensuring a balanced approach to security and privacy protection. The proposed scheme has been experimentally validated in a virtual environment and compared with existing solutions. Results demonstrate that DPCZK offers significant improvements in both effectiveness and efficiency.

Abstract Aiming at the problem of user’s task offloading in mobile edge computing and the potential leakage of location privacy during the offloading process, a privacy-preserving computing offloading scheme based on whale optimization algorithm is proposed. Using differential privacy technology to obfuscate the user's location information, the user can make task offloading decisions according to the obfuscated distance. Considering the delay, energy consumption, and their weighted sum, the offloading problem is modeled as a convex optimization problem. Then, the whale optimization algorithm is adopted to solve this optimization problem to achieve a balance between privacy protection and resource consumption. Experiments are conducted to verify the relationship between the degree of privacy leakage, the computation-offloading cost and real distance, privacy-preserving impact factor, the respective weights of time delay and energy consumption The experimental results show that the offloading scheme proposed in this paper has good performance in terms of cost and privacy protection.

More and more companies, institutions and organizations are choosing to put vast amounts of data on the cloud. However, applications such as multimedia office, e-government and e-health systems need to withhold some data in order to hide highly confidential information when uploading to the cloud. A new data integrity auditing scheme is proposed to protect the privacy information and share the data. The idea of sanitizable signature is used to sanitize the private data to protect the privacy and generate effective signatures to verify the integrity of the data. At the same time, the identity-based encryption auditing mechanism simplifies the complex certificate management and improves the audit efficiency. The stability of the scheme is verified by calculating the Computational Diffie-Hellman Problem (CDHP) and Discrete Logarithm Problem (DLP) in the stochastic prediction model. The performance of the proposed scheme is evaluated by simulation experiments, which proves that the proposed scheme is safe and effective.

Summary Location‐based services currently face two critical issues: an insufficient number of anonymous users and the problem of location semantic homogeneity. To prevent location homogeneity attacks, we suggest a blockchain‐based anonymization approach. This scheme introduces blockchain to store the anonymous process of the requesting user and collaborating user as evidence, establishes an incentive mechanism to promote cooperation between the two parties, and then selects users who meet the semantic threshold through the location semantic tree to construct the final anonymous set. The security analysis and simulation experiments demonstrate that the scheme suggested in this article can effectively motivate and constrain each user. The semantic security value is close to the maximum value of 1, preventing homogeneity attacks caused by location semantics and protecting users' location privacy.