18 citations · 39 across the 10 of their papers we have counts for
5 papers · 1 filter
Evaluation of Inference Attack Models for Deep Learning on Medical Data
Maoqiang Wu, Xinyue Zhang, Jiahao Ding +4
Deep learning has attracted broad interest in healthcare and medical communities. However, there has been little research into the privacy issues created by deep networks trained f…
Effective Proximal Methods for Non-convex Non-smooth Regularized Learning
Guannan Liang, Qianqian Tong, Jiahao Ding +2
Sparse learning is a very important tool for mining useful information and patterns from high dimensional data. Non-convex non-smooth regularized learning problems play essential r…
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees
Di Wang, Jiahao Ding, Lijie Hu +3
(Gradient) Expectation Maximization (EM) is a widely used algorithm for estimating the maximum likelihood of mixture models or incomplete data problems. A major challenge facing th…
Towards Plausible Differentially Private ADMM Based Distributed Machine Learning
Jiahao Ding, Jingyi Wang, Guannan Liang +2
The Alternating Direction Method of Multipliers (ADMM) and its distributed version have been widely used in machine learning. In the iterations of ADMM, model updates using local p…
Differentially Private and Fair Classification via Calibrated Functional Mechanism
Jiahao Ding, Xinyue Zhang, Xiaohuan Li +3
Machine learning is increasingly becoming a powerful tool to make decisions in a wide variety of applications, such as medical diagnosis and autonomous driving. Privacy concerns re…