Special Issues
Table of Content

Privacy-Enhancing Technologies for Secure Data Cooperation and Circulation

Submission Deadline: 30 June 2026 View: 544 Submit to Special Issue

Guest Editors

Dr. Yi Sun

Email: sybupt@bupt.edu.cn

Affiliation: School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, 100876, China

Homepage:

Research Interests: information security, data security, cyber security, privacy-enhancing technology

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Dr. Limei Peng

Email: auroraplm@knu.ac.kr

Affiliation: School of Computer Science and Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea

Homepage:

Research Interests: cloud/edge computing, wireless communications, IoT, data security

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Summary

Privacy-enhancing technologies, which are digital solutions that allow information to be collected, processed, analyzed, and shared while protecting data confidentiality and privacy, enable organizations to conduct joint data analysis in a privacy-friendly manner. The true power of privacy-enhancing technology is in keeping data "hidden" from researchers while at the same time enabling analysis of that data. The technologies could unlock new forms of collaboration and new norms in the responsible use of personal data. They may enable more collaboration across entities, sectors, and borders to help tackle shared challenges, helping drive solutions in areas such as health care, climate change, financial crime, human trafficking, and pandemic response.


Privacy-enhancing technologies offer several specific techniques to enhance privacy. Encryption techniques transform data into unreadable formats, ensuring that only authorized parties can access and decrypt the information. Anonymization methods remove personally identifiable information from datasets, making it challenging to identify specific individuals. Differential privacy techniques introduce noise or randomness to data analysis, preventing the identification of individual records.


Therefore, this special issue focuses on Privacy-Enhancing Technologies of Data Secure Cooperation and Circulation. The following subtopics are the particular interests of this special issue, including but not limited to:
· AI for enhanced data privacy
· Cybersecurity in visual data protection
· Image security
· Challenges in balancing privacy and data utility
· Data encryption and decryption
· Cybersecurity for privacy-preserving data ecosystems
· Privacy-preserving big data management
· Synthetic data generation and security
· Privacy-preserving techniques in data security
· Data anonymization
· Differential privacy theory & applications
· Federated learning for data security


Keywords

AI, image encryption, data management, CNN/RNN, industrial IoT, cyber security, industrial IoT

Published Papers


  • Open Access

    ARTICLE

    Heterogeneous User Authentication and Key Establishment Protocol for Client-Server Environment

    Huihui Zhu, Fei Tang, Chunhua Jin, Ping Wang
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073550
    (This article belongs to the Special Issue: Privacy-Enhancing Technologies for Secure Data Cooperation and Circulation)
    Abstract The ubiquitous adoption of mobile devices as essential platforms for sensitive data transmission has heightened the demand for secure client-server communication. Although various authentication and key agreement protocols have been developed, current approaches are constrained by homogeneous cryptosystem frameworks, namely public key infrastructure (PKI), identity-based cryptography (IBC), or certificateless cryptography (CLC), each presenting limitations in client-server architectures. Specifically, PKI incurs certificate management overhead, IBC introduces key escrow risks, and CLC encounters cross-system interoperability challenges. To overcome these shortcomings, this study introduces a heterogeneous signcryption-based authentication and key agreement protocol that synergistically integrates IBC for client More >

  • Open Access

    ARTICLE

    LUAR: Lightweight and Universal Attribute Revocation Mechanism with SGX Assistance towards Applicable ABE Systems

    Fei Tang, Ping Wang, Jiang Yu, Huihui Zhu, Mengxue Qin, Ling Yang
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.073423
    (This article belongs to the Special Issue: Privacy-Enhancing Technologies for Secure Data Cooperation and Circulation)
    Abstract Attribute-Based Encryption (ABE) has emerged as a fundamental access control mechanism in data sharing, enabling data owners to define flexible access policies. A critical aspect of ABE is key revocation, which plays a pivotal role in maintaining security. However, existing key revocation mechanisms face two major challenges: (1) High overhead due to ciphertext and key updates, primarily stemming from the reliance on revocation lists during attribute revocation, which increases computation and communication costs. (2) Limited universality, as many attribute revocation mechanisms are tailored to specific ABE constructions, restricting their broader applicability. To address these challenges,… More >

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