Guarding Machine Learning Hardware Against Physical Side-Channel Attacks

1 September 2021

Papers citing "Guarding Machine Learning Hardware Against Physical Side-Channel Attacks"

8 / 8 papers shown

Title
MACPruning: Dynamic Operation Pruning to Mitigate Side-Channel DNN Model Extraction Ruyi Ding Cheng Gongye Davis Ranney A. A. Ding Yunsi Fei AAML 63 0 0 24 Feb 2025
Defense against ML-based Power Side-channel Attacks on DNN Accelerators with Adversarial Attacks Xiaobei Yan Chip Hong Chang Tianwei Zhang AAML 26 1 0 07 Dec 2023
BlackJack: Secure machine learning on IoT devices through hardware-based shuffling Karthik Ganesan Michal Fishkin Ourong Lin Natalie Enright Jerger 19 4 0 26 Oct 2023
A Desynchronization-Based Countermeasure Against Side-Channel Analysis of Neural Networks J. Breier Dirmanto Jap Xiaolu Hou S. Bhasin AAML 11 8 0 25 Mar 2023
Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems Shail Dave Alberto Marchisio Muhammad Abdullah Hanif Amira Guesmi Aviral Shrivastava Ihsen Alouani Muhammad Shafique 23 13 0 18 Apr 2022
Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead Muhammad Shafique Mahum Naseer T. Theocharides C. Kyrkou O. Mutlu Lois Orosa Jungwook Choi OOD 71 100 0 04 Jan 2021
Cryptanalytic Extraction of Neural Network Models Nicholas Carlini Matthew Jagielski Ilya Mironov FedML MLAU MIACV AAML 65 134 0 10 Mar 2020
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware Florian Tramèr Dan Boneh FedML 112 395 0 08 Jun 2018