5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.CR2025
On the interplay of Explainability, Privacy and Predictive Performance with Explanation-assisted Model Extraction
Fatima Ezzeddine, Rinad Akel, Ihab Sbeity +3
Machine Learning as a Service (MLaaS) has gained important attraction as a means for deploying powerful predictive models, offering ease of use that enables organizations to levera…
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…