276 citations · 433 across the 16 of their papers we have counts for
5 papers · 1 filter
DP-UTIL: Comprehensive Utility Analysis of Differential Privacy in Machine Learning
Ismat Jarin, Birhanu Eshete
Differential Privacy (DP) has emerged as a rigorous formalism to reason about quantifiable privacy leakage. In machine learning (ML), DP has been employed to limit inference/disclo…
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…
Morphence: Moving Target Defense Against Adversarial Examples
Abderrahmen Amich, Birhanu Eshete
Robustness to adversarial examples of machine learning models remains an open topic of research. Attacks often succeed by repeatedly probing a fixed target model with adversarial e…
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…