273 citations · 914 across the 36 of their papers we have counts for
5 papers · 1 filter
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…
Auditing Differentially Private Machine Learning: How Private is Private SGD?
Matthew Jagielski, Jonathan Ullman, Alina Oprea
We investigate whether Differentially Private SGD offers better privacy in practice than what is guaranteed by its state-of-the-art analysis. We do so via novel data poisoning atta…
Subpopulation Data Poisoning Attacks
Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger +1
Machine learning systems are deployed in critical settings, but they might fail in unexpected ways, impacting the accuracy of their predictions. Poisoning attacks against machine l…
With Great Dispersion Comes Greater Resilience: Efficient Poisoning Attacks and Defenses for Linear Regression Models
Jialin Wen, Benjamin Zi Hao Zhao, Minhui Xue +2
With the rise of third parties in the machine learning pipeline, the service provider in "Machine Learning as a Service" (MLaaS), or external data contributors in online learning,…
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers
Giorgio Severi, Jim Meyer, Scott Coull +1
Training pipelines for machine learning (ML) based malware classification often rely on crowdsourced threat feeds, exposing a natural attack injection point. In this paper, we stud…