Mathematical Models for Data Privacy in Blockchain-Enabled Systems

Dear Colleagues,

As blockchain technologies continue to expand across diverse sectors, the protection of sensitive data in decentralized systems has emerged as a fundamental challenge. Mathematical modeling is central to addressing this challenge, offering robust frameworks for privacy guarantees, secure computation, and verifiable data handling across a variety of application domains.

This Special Issue seeks contributions that develop and analyze mathematical models for data privacy in blockchain-enabled systems, with applicability across fields such as finance, IoT, supply chains, energy, and digital identity. The aim is to explore both foundational models and application-driven methodologies that address key privacy concerns in decentralized architectures.

Topics of interest include, but are not limited to, the following:

• Formal privacy models for blockchain-based data systems;
• Differential privacy and secure multiparty computation on distributed ledgers;
• Privacy-preserving smart contracts and zero-knowledge proofs;
• Game-theoretic frameworks for data governance and access control;
• Cryptographic protocols for decentralized data sharing;
• Mathematical analysis of federated and decentralized learning systems;
• Optimization and verification of privacy policies in blockchain networks;
• Applications of privacy models in IoT, finance, and logistics.

We welcome both theoretical advancements and case studies that demonstrate the applicability of mathematical methods in real-world blockchain-enabled environments.

Dr. Ajay Kumar Shrestha
Guest Editor

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