3 papers
cs.CR2026
An Adaptive Gradient Clipping and Noise Injection Mechanism for Differentially Private Federated Learning
Wenjing Wei, Alla Jammine, Farid Nait-Abdesselam
Differentially private federated learning must balance privacy protection against model accuracy and training efficiency. Static gradient clipping applies a fixed threshold through…
cs.CR2026
Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation
Wenjing Wei, Farid Nait-Abdesselam, Alla Jammine
This article presents DDP-SA, a scalable privacy-preserving federated learning framework that jointly leverages client-side local differential privacy (LDP) and full-threshold addi…
cs.CR2025
Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection
Tianwei Lan, Luca Demetrio, Farid Nait-Abdesselam +2
Machine learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as a trusted source of malware annotations. But…