Paper 2023/076

Bake It Till You Make It: Heat-induced Leakage from Masked Neural Networks

Dev M. Mehta, Worcester Polytechnic Institute
Mohammad Hashemi, Worcester Polytechnic Institute
David S. Koblah, University of Florida
Domenic Forte, University of Florida
Fatemeh Ganji, Worcester Polytechnic Institute

Masking has become one of the most effective approaches for securing hardware designs against side-channel attacks. Irrespective of the effort put into correctly implementing masking schemes on a field programmable gate array (FPGA), leakage can be unexpectedly observed. This is due to the fact that the assumption underlying all masked designs, i.e., the leakages of different shares are independent of each other, may no longer hold in practice. In this regard, extreme temperatures have been shown to be an important factor in inducing leakage, even in correctly-masked designs. This has previously been verified using an external heat generator (i.e., a climate chamber). In this paper, we examine whether the leakage can be induced using the circuit components themselves. Specifically, we target masked neural networks (NNs) in FPGAs, with one of the main building blocks being block random access memory (BRAM) and flip-flops (FFs). In this respect, thanks to the inherent characteristics of NNs, our novel internal heat generators leverage solely the memories devoted to storing the user’s input, especially when frequently writing alternating patterns into BRAMs and FFs. The possibility of observing first-order leakage is evaluated by considering one of the most recent and successful first-order secure masked NNs, namely ModuloNET. ModuloNET is specifically designed for FPGAs, where BRAMs are used for storing the inputs and intermediate computations. Our experimental results demonstrate that undesirable first-order leakage can be observed by increasing the temperature when an alternating input is applied to the masked NN. To give a better understanding of the impact of extreme heat, we further perform a similar test on the design with FFs storing the input, where the same conclusion can be drawn.

Available format(s)
Publication info
Side-channel AnalysisMaskingNeural NetworksHeat GenerationT-testFPGA
Contact author(s)
dmmehta2 @ wpi edu
mhashemi @ wpi edu
dkoblah @ ufl edu
dforte @ ece ufl edu
fganji @ wpi edu
2023-01-23: approved
2023-01-22: received
See all versions
Short URL
Creative Commons Attribution-NonCommercial-NoDerivs


      author = {Dev M. Mehta and Mohammad Hashemi and David S. Koblah and Domenic Forte and Fatemeh Ganji},
      title = {Bake It Till You Make It: Heat-induced Leakage from Masked Neural Networks},
      howpublished = {Cryptology ePrint Archive, Paper 2023/076},
      year = {2023},
      note = {\url{}},
      url = {}
Note: In order to protect the privacy of readers, does not use cookies or embedded third party content.