57 citations · 143 across the 16 of their papers we have counts for
7 papers · 1 filter
Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks
Asim Waheed, Vasisht Duddu, Sebastian Szyller
Machine learning (ML) models are susceptible to various risks to security, privacy, and fairness. Most defenses are designed to protect against each risk individually (intended int…
Atlas: A Framework for ML Lifecycle Provenance & Transparency
Marcin Spoczynski, Marcela S. Melara, Sebastian Szyller
The rapid adoption of open source machine learning (ML) datasets and models exposes today's AI applications to critical risks like data poisoning and supply chain attacks across th…
SoK: Unintended Interactions among Machine Learning Defenses and Risks
Vasisht Duddu, Sebastian Szyller, N. Asokan
Machine learning (ML) models cannot neglect risks to security, privacy, and fairness. Several defenses have been proposed to mitigate such risks. When a defense is effective in mit…
False Claims against Model Ownership Resolution
Jian Liu, Rui Zhang, Sebastian Szyller +2
Deep neural network (DNN) models are valuable intellectual property of model owners, constituting a competitive advantage. Therefore, it is crucial to develop techniques to protect…
SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning
Vasisht Duddu, Sebastian Szyller, N. Asokan
Data used to train machine learning (ML) models can be sensitive. Membership inference attacks (MIAs), attempting to determine whether a particular data record was used to train an…
DAWN: Dynamic Adversarial Watermarking of Neural Networks
Sebastian Szyller, Buse Gul Atli, Samuel Marchal +1
Training machine learning (ML) models is expensive in terms of computational power, amounts of labeled data and human expertise. Thus, ML models constitute intellectual property (I…
PRADA: Protecting against DNN Model Stealing Attacks
Mika Juuti, Sebastian Szyller, Samuel Marchal +1
Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML models becomes paramount for two reasons: (a) a model can be a business advantag…