73 citations · 108 across the 6 of their papers we have counts for
12 papers
Bad Citrus: Reducing Adversarial Costs with Model Distances
Giorgio Severi, Will Pearce, Alina Oprea
Recent work by Jia et al., showed the possibility of effectively computing pairwise model distances in weight space, using a model explanation technique known as LIME. This method…
How to Combine Membership-Inference Attacks on Multiple Updated Models
Matthew Jagielski, Stanley Wu, Alina Oprea +2
A large body of research has shown that machine learning models are vulnerable to membership inference (MI) attacks that violate the privacy of the participants in the training dat…
Collaborative Information Sharing for ML-Based Threat Detection
Talha Ongun, Simona Boboila, Alina Oprea +4
Recently, coordinated attack campaigns started to become more widespread on the Internet. In May 2017, WannaCry infected more than 300,000 machines in 150 countries in a few days a…
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…
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…