2 citations · 3 across the 4 of their papers we have counts for
4 papers
Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation
Janvi Thakkar, Giulio Zizzo, Sergio Maffeis
Malicious adversaries can attack machine learning models to infer sensitive information or damage the system by launching a series of evasion attacks. Although various work address…
Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience
Janvi Thakkar, Giulio Zizzo, Sergio Maffeis
Machine learning models are being used in an increasing number of critical applications; thus, securing their integrity and ownership is critical. Recent studies observed that adve…
Adaptive Experimental Design for Intrusion Data Collection
Kate Highnam, Zach Hanif, Ellie Van Vogt +3
Intrusion research frequently collects data on attack techniques currently employed and their potential symptoms. This includes deploying honeypots, logging events from existing de…
Certified Federated Adversarial Training
Giulio Zizzo, Ambrish Rawat, Mathieu Sinn +2
In federated learning (FL), robust aggregation schemes have been developed to protect against malicious clients. Many robust aggregation schemes rely on certain numbers of benign c…