Leaky Nets: Recovering Embedded Neural Network Models and Inputs through Simple Power and Timing Side-Channels -- Attacks and Defenses

26 March 2021

Papers citing "Leaky Nets: Recovering Embedded Neural Network Models and Inputs through Simple Power and Timing Side-Channels -- Attacks and Defenses"

5 / 5 papers shown

Title
A Practical Introduction to Side-Channel Extraction of Deep Neural Network Parameters Raphael Joud Pierre-Alain Moëllic S. Pontié J. Rigaud AAML MIACV MLAU 21 13 0 10 Nov 2022
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 26 13 0 18 Apr 2022
Physical Side-Channel Attacks on Embedded Neural Networks: A Survey M. M. Real Ruben Salvador AAML 15 30 0 21 Oct 2021
A Review of Confidentiality Threats Against Embedded Neural Network Models Raphael Joud Pierre-Alain Moëllic Rémi Bernhard J. Rigaud 28 6 0 04 May 2021
Sapphire: A Configurable Crypto-Processor for Post-Quantum Lattice-based Protocols Utsav Banerjee T. Ukyab A. Chandrakasan 34 193 0 16 Oct 2019