Paper 2024/1521 Original: eprint.iacr.org/2024/1521 ↗

The SMAesH dataset

Gaëtan Cassiers
Charles Momin
Abstract

Datasets of side-channel leakage measurements are widely used in research to develop and benchmark side-channel attack and evaluation methodologies. Compared to using custom and/or one-off datasets, widely-used and publicly available datasets improve research reproducibility and comparability. Further, performing high-quality measurements requires specific equipment and skills, while also taking a significant amount of time. Therefore, using publicly available datasets lowers the barriers to entry into side-channel research. This paper introduces the SMAesH dataset. SMAesH is an optimized masked hardware implementation of the AES with a provably secure arbitrary-order masking scheme. The SMAesH dataset contains power traces of the first-order protected version of SMAesH acquired on two FPGAs from different generations, along with key, plaintext and masking randomness. A part of the dataset use uniformly random key and plaintext to enable leakage profiling, while another part uses a fixed key (still with uniformly random plaintext) to enable attack validation or leakage assessment in a fixed-versus-random setting. We document the experimental setup used to acquire the dataset and also discuss particular methods employed to maximize the information content in the leakage traces, such as power supply selection, fine-grained trace alignment and resolution optimization. We perfom an in-depth leakage analysis for the two targets. Finally, we briefly discuss the known attacks against the dataset.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Preprint.
History
2025-03-20: last updated
2024-09-27: published
Short URL
https://ia.cr/2024/1521
License
https://creativecommons.org/licenses/by/4.0/

BibTeX

@misc{cryptoeprint:2024/1521,
      author = {Gaëtan Cassiers and Charles Momin},
      title = {The SMAesH dataset},
      howpublished = {Cryptology {ePrint} Archive, Paper 2024/1521},
      year = {2024},
      url = {https://eprint.iacr.org/2024/1521}
}
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