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Paper 2023/1604

Manifold Learning Side-Channel Attacks against Masked Cryptographic Implementations

Jianye Gao, School of Cyber Science and Engineering, Wuhan Univeristy, Hubei, China
Xinyao Li, School of Cyber Science and Engineering, Wuhan Univeristy, Hubei, China
Changhai Ou, School of Cyber Science and Engineering, Wuhan Univeristy, Hubei, China
Zhu Wang, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
Fei Yan, School of Cyber Science and Engineering, Wuhan Univeristy, Hubei, China
Abstract

Masking, as a common countermeasure, has been widely utilized to protect cryptographic implementations against power side-channel attacks. It significantly enhances the difficulty of attacks, as the sensitive intermediate values are randomly partitioned into multiple parts and executed on different times. The adversary must amalgamate information across diverse time samples before launching an attack, which is generally accomplished by feature extraction (e.g., Points-Of-Interest (POIs) combination and dimensionality reduction). However, traditional POIs combination methods, machine learning and deep learning techniques are often too time consuming, and necessitate a significant amount of computational resources. In this paper, we undertake the first study on manifold learning and their applications against masked cryptographic implementations. The leaked information, which manifests as the manifold of high-dimensional power traces, is mapped into a low-dimensional space and achieves feature extraction through manifold learning techniques like ISOMAP, Locally Linear Embedding (LLE), and Laplacian Eigenmaps (LE). Moreover, to reduce the complexity, we further construct explicit polynomial mappings for manifold learning to facilitate the dimensionality reduction. Compared to the classical machine learning and deep learning techniques, our schemes built from manifold learning techniques are faster, unsupervised, and only require very simple parameter tuning. Their effectiveness has been fully validated by our detailed experiments.

Metadata
Available format(s)
PDF
Category
Attacks and cryptanalysis
Publication info
Preprint.
Keywords
manifold learningside-channel attacksmaskingdimensionality reductionmachine learning
Contact author(s)
2019302180014 @ whu edu cn
lxy06323 @ gmail com
History
2023-10-17: approved
2023-10-17: received
See all versions
Short URL
https://ia.cr/2023/1604
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2023/1604,
      author = {Jianye Gao and Xinyao Li and Changhai Ou and Zhu Wang and Fei Yan},
      title = {Manifold Learning Side-Channel Attacks against Masked Cryptographic Implementations},
      howpublished = {Cryptology {ePrint} Archive, Paper 2023/1604},
      year = {2023},
      url = {https://eprint.iacr.org/2023/1604}
}
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