152 citations · 379 across the 19 of their papers we have counts for
5 papers · 1 filter
Learning across Data Owners with Joint Differential Privacy
Yangsibo Huang, Haotian Jiang, Daogao Liu +3
In this paper, we study the setting in which data owners train machine learning models collaboratively under a privacy notion called joint differential privacy [Kearns et al., 2018…
EMA: Auditing Data Removal from Trained Models
Yangsibo Huang, Xiaoxiao Li, Kai Li
Data auditing is a process to verify whether certain data have been removed from a trained model. A recently proposed method (Liu et al. 20) uses Kolmogorov-Smirnov (KS) distance f…
IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks
Wei Qiu, Yangsibo Huang, Quanzheng Li
Missing value imputation is a challenging and well-researched topic in data mining. In this paper, we propose IFGAN, a missing value imputation algorithm based on Feature-specific…
MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery
Xiaoxiao Li, Yangsibo Huang, Binghui Peng +2
To address the issue that deep neural networks (DNNs) are vulnerable to model inversion attacks, we design an objective function, which adjusts the separability of the hidden data…
Privacy-preserving Learning via Deep Net Pruning
Yangsibo Huang, Yushan Su, Sachin Ravi +3
This paper attempts to answer the question whether neural network pruning can be used as a tool to achieve differential privacy without losing much data utility. As a first step to…