83 citations · 122 across the 6 of their papers we have counts for
10 papers
Scalable Extraction of Training Data from (Production) Language Models
Milad Nasr, Nicholas Carlini, Jonathan Hayase +7
This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training da…
No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"
Nicholas Carlini, Vitaly Feldman, Milad Nasr
New methods designed to preserve data privacy require careful scrutiny. Failure to preserve privacy is hard to detect, and yet can lead to catastrophic results when a system implem…
Debugging Differential Privacy: A Case Study for Privacy Auditing
Florian Tramer, Andreas Terzis, Thomas Steinke +3
Differential Privacy can provide provable privacy guarantees for training data in machine learning. However, the presence of proofs does not preclude the presence of errors. Inspir…
NeuraCrypt is not private
Nicholas Carlini, Sanjam Garg, Somesh Jha +3
NeuraCrypt (Yara et al. arXiv 2021) is an algorithm that converts a sensitive dataset to an encoded dataset so that (1) it is still possible to train machine learning models on the…
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning
Milad Nasr, Shuang Song, Abhradeep Thakurta +2
Differentially private (DP) machine learning allows us to train models on private data while limiting data leakage. DP formalizes this data leakage through a cryptographic game, wh…
Extracting Training Data from Large Language Models
Nicholas Carlini, Florian Tramer, Eric Wallace +9
It has become common to publish large (billion parameter) language models that have been trained on private datasets. This paper demonstrates that in such settings, an adversary ca…