4 papers
Potentials and Pitfalls of Applying Federated Learning in Hardware Assurance
Gijung Lee, Wavid Bowman, Olivia Dizon-Paradis +4
As microelectronics flourish and outsourcing of the design and manufacturing stages of integrated circuits (ICs) and printed circuit boards (PCBs) becomes the norm, microelectronic…
DECIFR: Domain-Aware Exfiltration of Circuit Information from Federated Gradient Reconstruction
Gijung Lee, Wavid Bowman, Olivia P. Dizon-Paradis +4
Federated Learning (FL) is a promising approach for multiparty collaboration as a privacy-preserving technique in hardware assurance, but its security against adversaries with doma…
A Data-Free Membership Inference Attack on Federated Learning in Hardware Assurance
Gijung Lee, Wavid Bowman, Olivia P. Dizon-Paradis +4
Federated Learning (FL) is an emerging solution to the data scarcity problem for training deep learning models in hardware assurance. While FL is designed to enhance privacy by not…
Debugging WebAssembly? Put some Whamm on it!
Elizabeth Gilbert, Matthew Schneider, Zixi An +5
Debugging and monitoring programs are integral to engineering and deploying software. Dynamic analyses monitor applications through source code or IR injection, machine code or byt…