4 papers
Scalable GPU-Based Integrity Verification for Large Machine Learning Models
Marcin Spoczynski, Marcela S. Melara
We present a security framework that strengthens distributed machine learning by standardizing integrity protections across CPU and GPU platforms and significantly reducing verific…
Threat Modeling for AI: The Case for an Asset-Centric Approach
Jose Sanchez Vicarte, Marcin Spoczynski, Mostafa Elsaid
Recent advances in AI are transforming AI's ubiquitous presence in our world from that of standalone AI-applications into deeply integrated AI-agents. These changes have been drive…
LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation
Abhinav Kumar, George Torres, Noah Guzinski +6
The privacy vulnerabilities of the federated learning (FL) paradigm, primarily caused by gradient leakage, have prompted the development of various defensive measures. Nonetheless,…
Atlas: A Framework for ML Lifecycle Provenance & Transparency
Marcin Spoczynski, Marcela S. Melara, Sebastian Szyller
The rapid adoption of open source machine learning (ML) datasets and models exposes today's AI applications to critical risks like data poisoning and supply chain attacks across th…