A novel privacy-preserving data sharing system based on attributed-based encryption and zero knowledge proof

Shivani (2025) A novel privacy-preserving data sharing system based on attributed-based encryption and zero knowledge proof. MSc(R) thesis, University of Glasgow.

Abstract

The exponential growth of digital data across various sectors, such as healthcare, finance, and e-commerce, has underscored critical concerns regarding data privacy, security, and ownership. Centralised data storage systems are inherently vulnerable to cyber-attacks, raising significant privacy risks and compliance challenges, despite regulatory frameworks like the General Data Protection Regulation (GDPR). This research introduces a decentralised, privacy-preserving data-sharing framework leveraging blockchain technology, Ciphertext-Policy Attribute-Based Encryption (CP-ABE), and Zero-Knowledge Proofs (ZKP).

By employing CP-ABE, the proposed system enables fine-grained access control, ensuring that only authorised entities can access sensitive data based on specified attributes. The integration of Zero-Knowledge Proofs preserves user privacy by allowing verification of access rights without revealing the underlying attributes. The system architecture is underpinned by decentralised storage, with smart contracts managing secure access verification.

Performance evaluations demonstrate that the system effectively handles dynamic policies and attribute sets, demonstrating its adaptability to real-world applications. This framework represents a significant advancement in privacy-preserving data-sharing technologies, offering a scalable and secure solution for safeguarding sensitive users’ attributes in decentralised environments.

• Item Type: Thesis (MSc(R))
• Qualification Level: Masters
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• Colleges/Schools: College of Science and Engineering > School of Computing Science
• Supervisor's Name: Truong, Dr. Nguyen
• Date of Award: 2025
• Depositing User: Theses Team
• Unique ID: glathesis:2025-85038
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 10 Apr 2025 15:00
• Last Modified: 10 Apr 2025 15:04
• Thesis DOI: 10.5525/gla.thesis.85038
• URI: https://theses.gla.ac.uk/id/eprint/85038