17 citations · 35 across the 7 of their papers we have counts for
7 papers
Netflix and Forget: Efficient and Exact Machine Unlearning from Bi-linear Recommendations
Mimee Xu, Jiankai Sun, Xin Yang +2
People break up, miscarry, and lose loved ones. Their online streaming and shopping recommendations, however, do not necessarily update, and may serve as unhappy reminders of their…
Label Inference Attack against Split Learning under Regression Setting
Shangyu Xie, Xin Yang, Yuanshun Yao +3
As a crucial building block in vertical Federated Learning (vFL), Split Learning (SL) has demonstrated its practice in the two-party model training collaboration, where one party h…
DPAUC: Differentially Private AUC Computation in Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on…
Differentially Private Multi-Party Data Release for Linear Regression
Ruihan Wu, Xin Yang, Yuanshun Yao +4
Differentially Private (DP) data release is a promising technique to disseminate data without compromising the privacy of data subjects. However the majority of prior work has focu…
Differentially Private AUC Computation in Vertical Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +3
Federated learning has gained great attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple parties. As a sub-category, vertical federa…
Differentially Private Label Protection in Split Learning
Xin Yang, Jiankai Sun, Yuanshun Yao +2
Split learning is a distributed training framework that allows multiple parties to jointly train a machine learning model over vertically partitioned data (partitioned by attribute…
Label Leakage and Protection from Forward Embedding in Vertical Federated Learning
Jiankai Sun, Xin Yang, Yuanshun Yao +1
Vertical federated learning (vFL) has gained much attention and been deployed to solve machine learning problems with data privacy concerns in recent years. However, some recent wo…