Voltage Scaling-Agnostic Counteraction of Side-Channel Neural Net Reverse Engineering via Machine Learning Compensation and Multi-Level Shuffling
- Resource Type
- Conference
- Authors
- Fang, Qiang; Lin, Longyang; Zhang, Hui; Wang, Tianqi; Alioto, Massimo
- Source
- 2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits) VLSI Technology and Circuits (VLSI Technology and Circuits), 2023 IEEE Symposium on. :1-2 Jun, 2023
- Subject
- Components, Circuits, Devices and Systems
Reverse engineering
Neural networks
Voltage
Side-channel attacks
Machine learning
Very large scale integration
HW security
machine learning
multi-level shuffling
- Language
- ISSN
- 2158-9682
This work proposes a voltage scaling-agnostic counteraction against neural network weight reverse engineering via side-channel attacks. Multi-level shuffling and machine learning-based dual power compensation are introduced. State-of-the-art protection ($\gt200\cdot 10^{6}$ MTD) is achieved at low power overhead (1.76$\times $) and zero latency overhead.