12 citations · 82 across the 51 of their papers we have counts for
6 papers · 1 filter
TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
Jacob Huckelberry, Yuke Zhang, Allison Sansone +3
Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique securit…
Island-based Random Dynamic Voltage Scaling vs ML-Enhanced Power Side-Channel Attacks
Dake Chen, Christine Goins, Maxwell Waugaman +2
In this paper, we describe and analyze an island-based random dynamic voltage scaling (iRDVS) approach to thwart power side-channel attacks. We first analyze the impact of the numb…
Unraveling Latch Locking Using Machine Learning, Boolean Analysis, and ILP
Dake Chen, Xuan Zhou, Yinghua Hu +5
Logic locking has become a promising approach to provide hardware security in the face of a possibly insecure fabrication supply chain. While many techniques have focused on lockin…
C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference
Yuke Zhang, Dake Chen, Souvik Kundu +3
Recently, private inference (PI) has addressed the rising concern over data and model privacy in machine learning inference as a service. However, existing PI frameworks suffer fro…
TriLock: IC Protection with Tunable Corruptibility and Resilience to SAT and Removal Attacks
Yuke Zhang, Yinghua Hu, Pierluigi Nuzzo +1
Sequential logic locking has been studied over the last decade as a method to protect sequential circuits from reverse engineering. However, most of the existing sequential logic l…
GF-Flush: A GF(2) Algebraic Attack on Secure Scan Chains
Dake Chen, Chunxiao Lin, Peter A. Beerel
Scan chains provide increased controllability and observability for testing digital circuits. The increased testability, however, can also be a source of information leakage for se…