Paper 2024/1521

The SMAesH dataset

Gaëtan Cassiers, UCLouvain
Charles Momin, UCLouvain
Abstract

Datasets of side-channel leakage measurements are widely used in research to develop and benchmarking 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 SMAesH on two FPGAs of 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. It is built from components that are widely available. We 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.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Preprint.
Keywords
side-channelpower leakagedatasetmaskingfpga
Contact author(s)
gaetan cassiers @ uclouvain be
charles momin @ uclouvain be
History
2024-09-30: approved
2024-09-27: received
See all versions
Short URL
https://ia.cr/2024/1521
License
Creative Commons Attribution
CC BY

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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