Side-Channel Attacks Target Machine Learning (ML) Models

Written by Paul Karazuba for Rambus Press A team of North Carolina State University researchers recently published a paper that highlights the vulnerability of machine learning (ML) models to side-channel attacks. Specifically, the team used power-based side-channel attacks to extract the secret weights of a Binarized Neural Network (BNN) in a highly-parallelized hardware implementation. “Physical … Continue reading Side-Channel Attacks Target Machine Learning (ML) Models