Technology & Infrastructure
Research Projects
Cybersecurity
Project Title: Unified Data Protection and Security Provenance in Digital Infrastructure for Fintech Services
Principal Investigator: Associate Professor Liang Zhenkai
Summary: This subproject aims to unify data protection and security provenance monitoring in the fintech infrastructure, where various systems collaborate for transactions. Current security solutions are platform-specific, leading to transaction discrepancies and integration challenges. Our goal is to establish consistent data protection across the stack, including identity authentication, access policy enforcement, data isolation, and privacy in processing. We propose a novel solution addressing challenges such as defining a consistent protection model, developing unified provenance models, and leveraging security mechanisms across platforms.
Privacy Preserving Synthetic Data Generation
Project Title: Proven Privacy Preserving Synthetic Data Generation with Generative Adversarial Networks and Differential Privacy
Principal Investigator: Associate Professor Biplab Sikdar
Summary: This project will develop highly representative tabular synthetic data from real data with different kinds of Generative Adversarial Networks (GANs) and Differential Privacy (DP) that is as-good-as real and has no privacy risks, thereby, making it compliant with data protection regulations worldwide. Unlike the one-to-one feature mapping that anonymization is based on, GANs use one-to-infinity feature mapping as they can learn the structure of a dataset. A classic example of this is DeepFakes1 that can be used to generate fake faces of people. However, GANs have only been investigated for unstructured data (image, video, and audio) for the past three years, whereas, this project will explore and study GANs for structured data (rows and columns) and text in unstructured data. Thus, the objective of this project is research and development of the following:
a. Different GAN models to generate synthetic data with different data types (categorical, numerical, and Boolean) for sequential as well as time series data.
b. Advanced features such as ability to model complex datasets that require a combination of relational joins between multiple tables, field constraints and time-series data, and text feature generation prior to generating synthetic data for free-text fields.
c. Data utility and data privacy metrics to create a benchmarking tool to evaluate the utility, fairness and risk of a dataset.
This project will strictly focus on applied research of synthetic data with GANs and privacy-preserving technology to ensure its research outcomes have a direct commercial impact for Financial Institutions (FIs) and the extended research community.
Blockchain-agnostic Interoperability with Capital Markets
Project Title: Secure and Privacy-preserving Blockchain-agnostic Interoperability with Capital Markets Protocol
Principal Investigator: Professor Ooi Beng Chin
Summary: This project focuses on building an enterprise-grade generic and blockchain-agnostic interoperability protocol in the form of a cross-chain bridge with security and privacy protection as key features. This bridge will implement InterOpera’s [InterOpera] proprietary Capital Market Protocol (CMP) targeting the use case of asset transfers between entities in the capital market and carbon credit market that belong to different blockchains. The project argues that the security and privacy of this protocol and its implementation need to be very high while ensuring compliance with regulations. To achieve privacy, the project study and develop state-of-the-art techniques based on Zero Knowledge Proof (ZKP) [Goldwasser19]. To enhance the security, we plan to develop techniques for program analysis and fuzzing, and machine learning for monitoring illicit activities. While being blockchain agnostic, this project will be evaluated with a few key blockchains, including public blockchains such as Ethereum and Chia Network, and enterprise blockchains such as Hyperledger Fabric and Cosmos. These blockchains are widely used in the capital market and the emerging carbon credit market.
Quantum speed-up in FinTech Algorithms and Optimization
Project Title: Quantum speed-up in FinTech Algorithms and Optimization
Principal Investigator: Associate Professor Ying Chen
Summary: This project aims to explore the potential of quantum algorithms and optimization in accelerating fintech applications, particularly in portfolio optimization and derivative pricing. The financial industry has experienced significant transformations with the emergence of fintech and the adoption of artificial intelligence and machine learning techniques. However, quantum computing presents new opportunities for more efficient processing.
In recent years, various quantum machine learning algorithms have been proposed, theoretically promising speed-ups over classical counterparts. However, many of these algorithms either require quantum access to data, raising questions about their applicability, or are heuristic in nature with no proven advantage over classical algorithms. Moreover, implementations have been limited to small-scale problems and basic settings where analytical solutions already exist, negating the need for advanced algorithms in practice.