Marco Di Gennaro
@marcodige.bsky.social
PhD Student @PoliMi, Security & Privacy of Machine and Federated Learning Researcher, 26, Italy.
📣 Our paper “On the (In)Security of Loading Machine Learning Models” has been accepted at IEEE S&P 2026 (13% acceptance rate this cycle). (1/5) 👇 Preprint: arxiv.org/abs/2509.06703 #ieeesp #ieeesp26 #ieee #cybersecurity #softwaresecurity #aisecurity #machinelearning #ml #zeroday
🚨 Our PoPETS 2025 work, "TimberStrike," finds that federated tree models are vulnerable to privacy leakage via dataset reconstruction. See you in Washington D.C. in July! 📄 Preprint: www.arxiv.org/abs/2506.07605 #FederatedLearning #Privacy #PoPETS
TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems
Federated Learning has emerged as a privacy-oriented alternative to centralized Machine Learning, enabling collaborative model training without direct data sharing. While extensively studied for neura...
arxiv.org