48 citations · 53 across the 5 of their papers we have counts for
5 papers
Practical Anonymous Two-Party Gradient Boosting Decision Tree
Chenyu Huang, Fan Zhang, Minxin Du +6
Structured data is well handled by gradient-boosted decision trees (GBDT), which are usually trained on vertically partitioned features across mutually distrustful parties. High sp…
SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding
Peihua Mai, Youlong Ding, Ziyan Lyu +2
Federated recommender system (FedRec) has emerged as a solution to protect user data through collaborative training techniques. A typical FedRec involves transmitting the full mode…
DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass
Minxin Du, Xiang Yue, Sherman S. M. Chow +3
Differentially private stochastic gradient descent (DP-SGD) adds noise to gradients in back-propagation, safeguarding training data from privacy leakage, particularly membership in…
Differential Privacy for Text Analytics via Natural Text Sanitization
Xiang Yue, Minxin Du, Tianhao Wang +3
Texts convey sophisticated knowledge. However, texts also convey sensitive information. Despite the success of general-purpose language models and domain-specific mechanisms with d…
Optimizing Privacy-Preserving Outsourced Convolutional Neural Network Predictions
Minghui Li, Sherman S. M. Chow, Shengshan Hu +3
Convolutional neural network is a machine-learning model widely applied in various prediction tasks, such as computer vision and medical image analysis. Their great predictive powe…