1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.CR2026★ 1 cited
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
Natalia Ponomareva, Zheng Xu, H. Brendan McMahan +12
High quality data is needed to unlock the full potential of AI for end users. However finding new sources of such data is getting harder: most publicly-available human generated da…
cs.LG2026
ACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control
Yuzheng Hu, Ryan McKenna, Da Yu +4
Generating high-quality synthetic text under differential privacy (DP) is critical for training and evaluating language models without compromising user privacy. Prior work on synt…
cs.CV2025
FADE: Adversarial Concept Erasure in Flow Models
Zixuan Fu, Yan Ren, Finn Carter +5
Diffusion models have demonstrated remarkable image generation capabilities, but also pose risks in privacy and fairness by memorizing sensitive concepts or perpetuating biases. We…