摘要：Many data are sensitive in areas such as finance, economics, political science, and life science. How to protect individual privacy when collecting and analyzing data has become even more significant and has raised growing public concerns in the age of big data because a huge amount of personal data are being generated and used almost freely every day. We propose an ER (encryption and recovery) algorithm that allows a central administration to do statistical inference based on the encrypted data, while still preserving each party's privacy even for a colluding majority in the presence of cyber attack. Theoretically, we essentially establish a general framework for privacy-preserving statistical inference, which can be viewed as the sensitive data based counterpart of traditional statistical inference assuming availability of the data. We demonstrate the applications of our algorithm to linear regression, logistic regression, maximum likelihood estimation, estimation of empirical distributions, and estimation of quantiles. Moreover, our algorithm can help to address another practically significant issue -- privacy preservation for distributed statistical inference when data are allocated to different parties who are unwilling to share their own data with others. Our algorithm is a promising “technology” that can be applied to overcome the difficulties of data analysis with privacy preservation not only in financial industry but also in other areas concerning people's privacy rights in the era of big data.

报告人介绍: Dr. Ning Cai is currently an associate professor in the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology. He is the first Academic Director of the new Master of Science program in FinTech jointly offered by School of Business and Management, School of Engineering, and School of Science. He received Ph.D. at Columbia University and both B.S. and M.S. in the School of Mathematical Sciences at Peking University.

Ning Cai's research interests include financial engineering, FinTech, data science, applied probability, and stochastic modeling. He has published papers in the top-tier journals such as Management Science, Operations Research, Mathematical Finance, and Mathematics of Operations Research. Currently, he serves as the associate editor for Operations Research, Operations Research Letters, Digital Finance, and IMA Journal of Management Mathematics, and also serves on the editorial board of Probability in the Engineering and Informational Sciences.