Financial Data Analysis with Privacy Preservation

蔡宁现任香港科技大学（广州）金融科技学域（系）教授及学域主任，同时担任广州市金融科技前沿研究重点实验室主任。之前曾于香港科技大学先后担任助理教授、副教授及教授。他于哥伦比亚大学获得运筹学博士学位，并于北京大学数学科学学院获得概率统计学士和硕士学位。他的研究方向主要包括金融科技、金融工程、风险管理以及金融和经济中的随机建模。他现在担任Operations Research Letters金融工程领域的领域主编，并曾于2015年至2023年担任Operations Research的副主编。他的研究成果曾发表于多个顶级学术期刊，包括Management Science、Operations Research、Mathematics of Operations Research、Mathematical Finance和INFORMS Journal on Computing。

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.