papers

Publications (7)

cs.CR2024

Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud

Marcin Chrapek, Anjo Vahldiek-Oberwagner, Marcin Spoczynski +3

Foundation Models (FMs) display exceptional performance in tasks such as natural language processing and are being applied across a growing range of disciplines. Although typically…

cs.CR2025

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…

cs.CR2025

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,…

cs.CR2025

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…

cs.DC2026

Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU

Marcin Spoczynski, Daniel Fleischer, Moshe Berchansky +5

Porting deep learning algorithms to new hardware accelerators requires developers to repeatedly apply the same low-level optimizations -- quantization, memory access coalescing, ti…

cs.CR2025

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…

cs.NI2022

Optimal Dynamic Orchestration in NDN-based Computing Networks

Hao Feng, Yi Zhang, Srikathyayani Srikanteswara +4

Named Data Networking (NDN) offers promising advantages in deploying next-generation service applications over distributed computing networks. We consider the problem of dynamic or…