11 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.LG2023
A Unified View of Differentially Private Deep Generative Modeling
Dingfan Chen, Raouf Kerkouche, Mario Fritz
The availability of rich and vast data sources has greatly advanced machine learning applications in various domains. However, data with privacy concerns comes with stringent regul…
cs.LG2023
MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regime
Tejumade Afonja, Dingfan Chen, Mario Fritz
The potential of realistic and useful synthetic data is significant. However, current evaluation methods for synthetic tabular data generation predominantly focus on downstream tas…
cs.LG2022★ 11 cited
RelaxLoss: Defending Membership Inference Attacks without Losing Utility
Dingfan Chen, Ning Yu, Mario Fritz
As a long-term threat to the privacy of training data, membership inference attacks (MIAs) emerge ubiquitously in machine learning models. Existing works evidence strong connection…