Jul 2026· 2026 5th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)· pp. 1-8· 0 citations· 16 references
Abstract
The growing demand for large models in industrial Internet necessitates secure sharing of cloud-stored training data. However, existing Ciphertext-Policy Attribute-Based Encryption schemes suffer from low efficiency and insufficient accuracy in tracing key-leaking users. We propose a traceable secure data sharing scheme based on credibility, which enforces dual access control via credibility and attributes. It strengthens the binding of user and key and integrates a large model to create an intelligent traceability framework, utilizing multi-layer query mechanisms and Merkle trees to reduce redundant verification operations and improve tracing efficiency. Under the DBDH assumption, the scheme is IND-CPA secure. Experiments on the “Insider Threat Test Dataset” demonstrate a traceability accuracy of 99.46%, significantly reducing unnecessary verification operations. The proposed scheme provides an efficient, accurate, and practical solution for secure data sharing and malicious user traceability in distributed industrial cloud environments.
A privacy-preserving, secure data-sharing framework tailored for edge-cloud collaborative architectures that minimizes the computational overhead on the terminal side while safeguarding user privacy, and effectively reduces the overhead associated with user joining and revocation within the same group.
Qikun Zhang, Zheng Cai, Jinbo Feng et al.· Journal of King Saud Univers...· 0 citations
Addressing the limitations of existing encryption methods that rely on a single fixed key and struggle to accommodate the diverse access and secure storage demands of massive network cloud data, this paper proposes a secure storage method for massive network cloud data based on the CP-ABE algorithm. A data distribution strategy based on consistent hashing and dynamic weight adjustment is designed, deploying a multi-copy redundant storage network to achieve efficient storage and access. Access policies are formulated based on data attributes, and the CP-ABE algorithm enables precise matching between user attributes and policies, supporting fine-grained permission control. A hierarchical key management mechanism based on elliptic curves is implemented, combined with dynamic update policies, to ensure data security throughout its entire lifecycle. Experiments demonstrate: When data scale increases to 200GB, the encryption time of this method rises gradually. In terms of storage integrity and high-concurrency read/write performance, this method significantly outperforms the comparison methods. By deeply integrating attribute encryption with a dynamic storage architecture, this approach enhances system scalability and access control flexibility while ensuring security.
Dan Pan, Yuzhang Lin, Luxin Lin et al.· Digital Signal and Computer...· 0 citations
A viable, privacy-friendly auditing framework of clouds which guarantees the end-toend encrypted verification without sacrificing the efficiency is presented.
Deepshikha Chaturvedi, Vidyullata Devmane, Shashikant S. Radke et al.· International Journal of Com...· 0 citations
A novel VMKSE scheme (VMKSE-BFF) is presented by adopting BFF, which can simultaneously support verifiability of and secure data sharing in a multi-user setting and a comparison with the existing VMKSE schemes is provided.
Yandong Su, Bing-Hang Wang, Yan-Jie Xiang et al.· Mathematics· 0 citations
A secure cloud computing scheme based on blockchain technology and DL based intrusion detection with the purpose to realize privacy-preserving cloud data management and may yield promising results in security cloud infrastructures for healthcare, and financial system as well as more intelligent enterprise(ies) in general.
Soujenya.voggu, Devarapu Shiva Prasad, Pasam Ramu et al.· International journal of com...· 0 citations
Federated learning enables collaborative model training between central servers and distributed clients without collecting users' raw sensitive data, which effectively promotes the large-scale deployment of intelligent collaborative services. Considering the high sensitivity of local training data and model gradient parameters in federated learning, protecting identity privacy and interaction security has become extremely critical. Therefore, mutual identity authentication is indispensable to restrict illegal client access and prevent malicious parameter transmission and data tampering. In this paper, we propose a lightweight anonymous authentication scheme for federated learning (FedLAS), which realizes secure mutual authentication between servers and clients and establishes a shared session key for subsequent encrypted interaction. In particular, the proposed scheme eliminates the reliance on high-cost cryptographic operations such as bilinear pairing, thus minimizing computational and communication overhead. Furthermore, informal security analysis demonstrates that our FedLAS scheme can resist multiple common attacks and meet predefined security requirements. Extensive comparative experiments show that the FedLAS scheme achieves excellent performance in computational and communication cost. It is well suitable for resource-constrained federated learning scenarios.
Shu Wu, Guoqiang Meng, Linlin Lu et al.· Scientific Reports· 0 citations
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