The 49th Frontier Forum for Digital Technology and Finance

Abstract：

Privacy-preserving data analysis is gaining significant attention in the current era of big data, driven by growing public concerns about data privacy. This focus is particularly crucial for financial data analysis, given the heightened importance of digital finance and FinTech as well as the sensitive nature of financial data. We develop a series of privacy-preserving algorithms to address various critical aspects of data analysis, including data collection, statistical inference, and resampling. The privacy preservations of these algorithms are justified theoretically in the sense of carefully formulated definitions. Furthermore, we demonstrate the applications of these algorithms to machine learning.   

Speaker Introduction：

Ning Cai currently serves as Professor and Thrust Leader of the FinTech Thrust at The Hong Kong University of Science and Technology (Guangzhou), a National-Level High-Level Talent, and concurrently Director of the Guangzhou Key Laboratory of Cutting-Edge Research in Financial Technology. Previously, he served successively as Assistant Professor, Associate Professor, and Professor at The Hong Kong University of Science and Technology. He received his Ph.D. in Operations Research from Columbia University, and his Bachelor's and Master's degrees in Probability and Statistics from Peking University. His research interests mainly include financial technology, financial engineering, green finance, and stochastic modeling in finance and economics. He currently serves as the Area Editor for Financial Engineering of Operations Research Letters and previously served as an Associate Editor of Operations Research from 2015 to 2023. His research results have been published in several top academic journals, including Management Science, Operations Research, Mathematics of Operations Research, Mathematical Finance, and INFORMS Journal on Computing.