276 citations · 373 across the 6 of their papers we have counts for
10 papers
MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members
Ismat Jarin, Birhanu Eshete
In membership inference attacks (MIAs), an adversary observes the predictions of a model to determine whether a sample is part of the model's training data. Existing MIA defenses c…
Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problem
Abderrahmen Amich, Birhanu Eshete
Despite multiple efforts made towards robust machine learning (ML) models, their vulnerability to adversarial examples remains a challenging problem that calls for rethinking the d…
EG-Booster: Explanation-Guided Booster of ML Evasion Attacks
Abderrahmen Amich, Birhanu Eshete
The widespread usage of machine learning (ML) in a myriad of domains has raised questions about its trustworthiness in security-critical environments. Part of the quest for trustwo…
Explanation-Guided Diagnosis of Machine Learning Evasion Attacks
Abderrahmen Amich, Birhanu Eshete
Machine Learning (ML) models are susceptible to evasion attacks. Evasion accuracy is typically assessed using aggregate evasion rate, and it is an open question whether aggregate e…
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting
Ismat Jarin, Birhanu Eshete
When multiple parties that deal with private data aim for a collaborative prediction task such as medical image classification, they are often constrained by data protection regula…
Best-Effort Adversarial Approximation of Black-Box Malware Classifiers
Abdullah Ali, Birhanu Eshete
An adversary who aims to steal a black-box model repeatedly queries the model via a prediction API to learn a function that approximates its decision boundary. Adversarial approxim…