Enhanced Tower Field Mask Scheme with Affine Transformation-based Dynamic S-box

Masking countermeasures are robust solutions applied to cryptographic devices to improve their side-channel analysis resistance. Implementing substitution boxes (S-boxes) and using an efficient mask scheme are essential for improving security in modern ciphers. Consequently, this paper proposes an improved tower field mask scheme with an affine transformation-based dynamic S-box. The approach resists both Correlation Power Analysis attacks with Hamming Weight and Hamming Distance models, even when employing up to two million power traces. The measurement-to-disclosure improvement for the extracted key byte is at least 158× higher than the previous scheme, while our hardware overhead is around 1.04×. Furthermore, the proposal enhances the devices’ resistance to recent Deep-Learning Side-Channel Analyses.

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Published
2025-05-25
DOI
https://doi.org/10.1109/iscas56072.2025.11044135
Citations
1
Primary Topic
Advanced Numerical Analysis Techniques
Type
article
Field-Weighted Citation Impact
1.33
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Enhanced Tower Field Mask Scheme with Affine Transformation-based Dynamic S-box

Thai-Ha Tran, Trong-Thuc Hoang, Cong‐Kha Pham, Van‐Phuc Hoang et al.
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Advanced Numerical Analysis Techniques
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article

Enhanced Tower Field Mask Scheme with Affine Transformation-based Dynamic S-box

Thai-Ha Tran, Trong-Thuc Hoang, Cong‐Kha Pham, Van‐Phuc Hoang, Duc-Thuan Dam
article en
1 citations

Abstract

Masking countermeasures are robust solutions applied to cryptographic devices to improve their side-channel analysis resistance. Implementing substitution boxes (S-boxes) and using an efficient mask scheme are essential for improving security in modern ciphers. Consequently, this paper proposes an improved tower field mask scheme with an affine transformation-based dynamic S-box. The approach resists both Correlation Power Analysis attacks with Hamming Weight and Hamming Distance models, even when employing up to two million power traces. The measurement-to-disclosure improvement for the extracted key byte is at least 158× higher than the previous scheme, while our hardware overhead is around 1.04×. Furthermore, the proposal enhances the devices’ resistance to recent Deep-Learning Side-Channel Analyses.

Le Quy Don Technical University (VN), University of Electro-Communications (JP)
Sustainable cities and communities
Openalex Percentile: Top 22%
Advanced Numerical Analysis Techniques
1.33
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