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Conditional Variational AutoEncoder based on Stochastic Attacks

Authors:
Gabriel Zaid , Thales ITSEF, Toulouse, France
Lilian Bossuet , Univ Lyon, UJM-Saint-Etienne, CNRS Laboratoire Hubert Curien UMR 5516 F-42023, Saint-Etienne, France
Mathieu Carbone , Thales ITSEF, Toulouse, France
Amaury Habrard , Univ Lyon, UJM-Saint-Etienne, CNRS Laboratoire Hubert Curien UMR 5516 F-42023, Saint-Etienne, France; Institut Universitaire de France (IUF), Paris, France
Alexandre Venelli , NXP Semiconductors, France
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DOI: 10.46586/tches.v2023.i2.310-357
URL: https://tches.iacr.org/index.php/TCHES/article/view/10286
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Abstract: Over the recent years, the cryptanalysis community leveraged the potential of research on Deep Learning to enhance attacks. In particular, several studies have recently highlighted the benefits of Deep Learning based Side-Channel Attacks (DLSCA) to target real-world cryptographic implementations. While this new research area on applied cryptography provides impressive result to recover a secret key even when countermeasures are implemented (e.g. desynchronization, masking schemes), the lack of theoretical results make the construction of appropriate and powerful models a notoriously hard problem. This can be problematic during an evaluation process where a security bound is required. In this work, we propose the first solution that bridges DL and SCA in order to get this security bound. Based on theoretical results, we develop the first Machine Learning generative model, called Conditional Variational AutoEncoder based on Stochastic Attacks (cVAE-SA), designed from the well-known Stochastic Attacks, that have been introduced by Schindler et al. in 2005. This model reduces the black-box property of DL and eases the architecture design for every real-world crypto-system as we define theoretical complexity bounds which only depend on the dimension of the (reduced) trace and the targeting variable over F2n . We validate our theoretical proposition through simulations and public datasets on a wide range of use cases, including multi-task learning, curse of dimensionality and masking scheme.
BibTeX
@article{tches-2023-33040,
  title={Conditional Variational AutoEncoder based on Stochastic Attacks},
  journal={IACR Transactions on Cryptographic Hardware and Embedded Systems},
  publisher={Ruhr-Universit├Ąt Bochum},
  volume={2023, Issue 2},
  pages={310-357},
  url={https://tches.iacr.org/index.php/TCHES/article/view/10286},
  doi={10.46586/tches.v2023.i2.310-357},
  author={Gabriel Zaid and Lilian Bossuet and Mathieu Carbone and Amaury Habrard and Alexandre Venelli},
  year=2023
}