5 citations · 7 across the 4 of their papers we have counts for
4 papers
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…
Private prediction for large-scale synthetic text generation
Kareem Amin, Alex Bie, Weiwei Kong +5
We present an approach for generating differentially private synthetic text using large language models (LLMs), via private prediction. In the private prediction framework, we only…
DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation
Yunjuan Wang, Hussein Hazimeh, Natalia Ponomareva +3
Distribution shifts and adversarial examples are two major challenges for deploying machine learning models. While these challenges have been studied individually, their combinatio…
Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models
Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva +3
We address the challenge of ensuring differential privacy (DP) guarantees in training deep retrieval systems. Training these systems often involves the use of contrastive-style los…