2.3k citations · 7.1k across the 22 of their papers we have counts for
3 papers · 1 filter
Harnessing large-language models to generate private synthetic text
Alexey Kurakin, Natalia Ponomareva, Umar Syed +2
Differentially private training algorithms like DP-SGD protect sensitive training data by ensuring that trained models do not reveal private information. An alternative approach, w…
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin +6
ML models are ubiquitous in real world applications and are a constant focus of research. At the same time, the community has started to realize the importance of protecting the pr…
RETVec: Resilient and Efficient Text Vectorizer
Elie Bursztein, Marina Zhang, Owen Vallis +2
This paper describes RETVec, an efficient, resilient, and multilingual text vectorizer designed for neural-based text processing. RETVec combines a novel character encoding with an…