| Author: | Huang, Zhangcheng |
| Title: | A proposed privacy-preserving platform as a data sharing solution in the financial field |
| Advisors: | Tong, Wilson (AF) Li, Qing (COMP) |
| Degree: | DFinTech |
| Year: | 2024 |
| Subject: | Computer networks -- Security measures Data protection Financial services industry -- Technological innovations Financial services industry -- Security measures Hong Kong Polytechnic University -- Dissertations |
| Department: | Faculty of Business |
| Pages: | ix, 232 pages : color illustrations |
| Language: | English |
| Abstract: | The digital economy, driven by technologies such as big data and artificial intelligence, relies heavily on data, often referred to as the “fifth factor of production.” However, with the increasing importance of data, concerns over data sharing and privacy have grown, especially in the financial industry, where data are sensitive. This thesis presents a comprehensive study on challenges in today’s data sharing and proposes a privacy-preserving computing platform (named BEEHIVE platform) as the solution for secure data sharing and analysis. The thesis discusses the challenges faced in implementing data sharing across four critical financial domains, including regulatory compliance, risk control, insurance pricing, and marketing. It highlights the need for a platform that facilitates data sharing while ensuring data privacy and security, introducing the BEEHIVE platform as a solution. The thesis also presents innovative technical improvements designed to enhance performance and meet the practical needs of financial scenarios. The thesis further explores the platform’s application in various financial-use cases, such as assisting regulatory authorities, enhancing credit risk control, optimizing auto insurance pricing, and improving advertising marketing. The platform’s performance and business experiments demonstrate its efficiency and effectiveness in handling large scales of financial data while maintaining privacy. The thesis also highlights the theoretical and practical implications of privacy computing in the financial field. It expands the division of labor theory by introducing data division of labor and emphasizes the importance of privacy computing in enhancing data operations and financial services. The thesis concludes that the BEEHIVE platform significantly contributes to the financial field by providing a secure and efficient way to process and analyze data, thereby fostering innovation and development in the digital economy. Despite the platform’s advantages, the thesis acknowledges limitations such as performance efficiency, data integration, and standardization, cost, as well as the need for future research directions. In summary, the thesis underscores the transformative potential of privacy-preserving platforms such as BEEHIVE in the financial industry, offering a robust framework that addresses the complex balance between data sharing and privacy protection. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 7807.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.77 MB | Adobe PDF | View/Open |
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