Graduation Term

3-25-2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

School of Information Technology: Information Systems

Committee Chair

Yongning Tang

Committee Member

Chung-Chih Li

Abstract

In an era where Artificial Intelligence (AI) and the Internet of Things (IoT) are pushing data to the forefront of industrial and academic pursuits, the dynamics of data trading are rapidly evolving. Blockchain technology, celebrated for its inherent security features like non-repudiation and traceability, plays a critical role in this transformation. However, integrating blockchain into data trading platforms presents significant challenges, especially in achieving the sophisticated security properties required for practical applications.

This thesis introduces a blockchain-based Secure Data Trading (SDT) protocol, crafted to refine the security frameworks of data exchange platforms by emphasizing two critical aspects: data multiple sellability of data and data verification. Data multiple sellability allows for the repeated sale of the same data item to multiple buyers without integrity compromise, while data verification empowers buyers to ascertain data quality before finalizing transactions. The SDT protocol incorporates advanced cryptographic techniques and a novel transaction framework to address these aspects effectively.

The theoretical underpinnings of the SDT protocol are extensively detailed, elucidating its operational phases and the security measures integrated at each step. Empirical assessments are presented, comparing the SDT protocol against contemporary counterparts, to demonstrate its capability in ensuring the integrity and confidentiality of data transactions. This protocol not only showcases effectiveness in safeguarding the highlighted security properties but also excels in efficiency and cost-effectiveness, potentially setting new standards for blockchain-based data trading platforms.

The implications of this research are vast, offering a scalable and adaptable solution that could revolutionize data trading across various sectors influenced by AI and IoT, thereby aligning with the aspirations of Industry 4.0 and beyond.

DOI

https://doi.org/10.30707/ETD2024.20240618063947536031.999999

Page Count

111

Available for download on Sunday, May 31, 2026

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