Privacy-Preserving Techniques in AI, Blockchain and Cloud Systems with Formal Mathematical Analysis

Dear Colleagues,

The widespread adoption of artificial intelligence (AI), blockchain, and cloud technologies necessitates the addressing of security and privacy concerns and appropriate mathematical analysis to ensure the robustness and integrity of computer systems. The integration of AI, blockchain and cloud techniques with formal mathematical analysis opens up new possibilities for enhancing security, privacy, and trust in various applications. However, this integration also introduces novel challenges, such as customized-but-rigorous security analysis under appropriate mathematical hardness assumptions, preserving privacy federated learning on non-independent and identically distributed data, securing smart contracts against malicious participants, etc. Addressing these challenges requires a multidisciplinary approach that combines insights from mathematics, computer science and cybersecurity.

This Special Issue aims to bring together researchers and practitioners to tackle these research frontiers and challenges. It encourages the exploration of innovative methodologies, definitions, frameworks, and practical solutions that integrate AI, blockchain, cloud, security, and privacy, strengthened by formal mathematical analysis. By fostering collaboration between academia and industry, this Special Issue seeks to contribute to the development of secure, privacy-preserving and trustworthy application systems. Topics of interest for this Special Issue include, but are not limited to, the following:

• AI-driven threat detection and mitigation in blockchain networks and cloud communications, utilizing statistical methods and machine learning techniques.
• Privacy-enhancing cryptography for AI, blockchain, and cloud applications, with a focus on the mathematical foundations of cryptographic protocols and secure multi-party computation.
• Scalability and performance optimization in AI, blockchain, and cloud integration, applying mathematical optimization and queuing theory.
• Privacy-enhanced identification and identity management on decentralized and centralized platforms, leveraging information theory and differential privacy
• Privacy-preserving federated learning in distributed environments, incorporating secure aggregation and homomorphic encryption.
• Secure and privacy-aware data sharing in AI, blockchain and cloud
• Trust and reputation mechanisms in AI, blockchain and cloud systems.
• Security and integrity of blockchain-based smart contracts.
• Privacy and security challenges in flexible and scalable applications (e.g., healthcare, finance, supply chain), exploring the mathematical foundations of risk assessment and threat modeling.
• Definitions, theorems, and frameworks in provable security and symbolic analysis.

Dr. Jialing He
Prof. Zhi Fang
Dr. Chunhai Li
Guest Editors

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