2022 journal article
Guarding Machine Learning Hardware Against Physical Side-channel Attacks
ACM JOURNAL ON EMERGING TECHNOLOGIES IN COMPUTING SYSTEMS, 18(3).
Machine learning (ML) models can be trade secrets due to their development cost. Hence, they need protection against malicious forms of reverse engineering (e.g., in IP piracy). With a growing shift of ML to the edge devices, in part for performance and in part for privacy benefits, the models have become susceptible to the so-called physical side-channel attacks.