Data has become a critical factor of production, yet its circulation and value realization face fundamental challenges in trustworthiness, privacy, security, and regulatory compliance. Isolated techniques like encryption or anonymization are insufficient for complex real-world scenarios involving multi-party collaborative computing, data sovereignty assurance, circulation traceability, auditing, and reliability in adversarial environments. Recent advancements in privacy computing, such as Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), Differential Privacy (DP), Trusted Execution Environments (TEEs), and Federated Learning (FL)—combined with blockchain, offer novel paradigms and potentials for constructing next-generation trustworthy and private data circulation infrastructure.
This Special Issue aims to collate cutting-edge research on the deep integration of diverse privacy computing techniques with trustworthy computing mechanisms to address end-to-end trust and privacy preservation in cross-domain, cross-institutional data element circulation. We solicit high-quality original papers focusing on theoretical innovations, system building, practical deployment, and evaluation.
Topics of interest include:
For author information and guidelines on submission criteria, please visit the Author Information Page. Please submit papers through the IEEE Author Portal, and be sure to select the special-issue name. Manuscripts should be written in English and describe original research. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, The review process will comply with the standard review process of the IEEE Transactions on Dependable and Secure Computing journal. All submitted papers will be evaluated on the basis of relevance, significance of contribution, and technical quality.
In addition to submitting your paper to IEEE Transactions on Dependable and Secure Computing, you are also encouraged to upload the data related to your paper to IEEE DataPort. IEEE DataPort is IEEE's data platform that supports the storage and publishing of datasets while also providing access to thousands of research datasets. Uploading your dataset to IEEE DataPort will strengthen your paper and will support research reproducibility. Your paper and the dataset can be linked, providing a good opportunity for you to increase the number of citations you receive. Data can be uploaded to IEEE DataPort prior to submitting your paper or concurrent with the paper submission. Thank you!
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