21 citations · 27 across the 8 of their papers we have counts for
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cs.LG2024★ 5 cited
Privacy Implications of Explainable AI in Data-Driven Systems
Fatima Ezzeddine
Machine learning (ML) models, demonstrably powerful, suffer from a lack of interpretability. The absence of transparency, often referred to as the black box nature of ML models, un…
cs.LG2024
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability
Fatima Ezzeddine, Mirna Saad, Omran Ayoub +5
Anomaly detection (AD), also referred to as outlier detection, is a statistical process aimed at identifying observations within a dataset that significantly deviate from the expec…
cs.LG2024★ 1 cited
Knowledge Distillation-Based Model Extraction Attack using GAN-based Private Counterfactual Explanations
Fatima Ezzeddine, Omran Ayoub, Silvia Giordano
In recent years, there has been a notable increase in the deployment of machine learning (ML) models as services (MLaaS) across diverse production software applications. In paralle…