100 citations · 226 across the 9 of their papers we have counts for
9 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…
Backdoor Attacks for In-Context Learning with Language Models
Nikhil Kandpal, Matthew Jagielski, Florian Tramèr +1
Because state-of-the-art language models are expensive to train, most practitioners must make use of one of the few publicly available language models or language model APIs. This…
A Note On Interpreting Canary Exposure
Matthew Jagielski
Canary exposure, introduced in Carlini et al. is frequently used to empirically evaluate, or audit, the privacy of machine learning model training. The goal of this note is to prov…
Privacy Auditing with One (1) Training Run
Thomas Steinke, Milad Nasr, Matthew Jagielski
We propose a scheme for auditing differentially private machine learning systems with a single training run. This exploits the parallelism of being able to add or remove multiple t…
Students Parrot Their Teachers: Membership Inference on Model Distillation
Matthew Jagielski, Milad Nasr, Christopher Choquette-Choo +2
Model distillation is frequently proposed as a technique to reduce the privacy leakage of machine learning. These empirical privacy defenses rely on the intuition that distilled ``…
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators
Keane Lucas, Matthew Jagielski, Florian Tramèr +2
It is becoming increasingly imperative to design robust ML defenses. However, recent work has found that many defenses that initially resist state-of-the-art attacks can be broken…