Abstract: Data contain valuable information that could play important roles in decision making, but many data are sensitive in areas such as finance, economics, and life science. How to collect and analyze data while still protecting individual privacy has attracted increasing attention from researchers, practitioners, and policymakers. We propose a privacy-preserving data collection algorithm that allows a central administration to collect the data set while still preserving individual privacy. Moreover, we demonstrate the applications of our algorithm to machine learning.

Short Bio: Ning Cai is currently professor and thrust head of the Thrust of Financial Technology at the Hong Kong University of Science and Technology (Guangzhou) (HKUST(GZ)). Previously, he taught at HKUST as assistant professor, associate professor, and professor sequentially. He received both MS and PhD at Columbia University and both BS and MS at Peking University. His research interests include FinTech, financial engineering, applied probability, and stochastic modeling. He serves as associate editor or editorial board member of seven international scholarly journals, including Operations Research, Operations Research Letters,  Stochastic Models, and Digital Finance. 

Tencent:
https://meeting.tencent.com/dm/1pXsXOXxOCjh
ID：707-648-078