[Resource Topic] 2023/1856: Optimizing AES Threshold Implementation under the Glitch-Extended Probing Model

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Optimizing AES Threshold Implementation under the Glitch-Extended Probing Model

Authors: Fu Yao, Hua Chen, Yongzhuang Wei, Enes Pasalic, Feng Zhou, Limin Fan


Threshold Implementation (TI) is a well-known Boolean masking technique that provides provable security against side-channel attacks. In the presence of glitches, the probing model was replaced by the so-called glitch-extended probing model which specifies a broader security framework. In CHES 2021, Shahmirzadi et al. introduced a general search method for finding first-order 2-share TI schemes without fresh randomness (under the presence of glitches) for a given encryption algorithm. Although it handles well single-output Boolean functions, this method has to store output shares in registers when extended to vector Boolean functions, which results in more chip area and increased latency. Therefore, the design of TI schemes that have low implementation cost under the glitch-extended probing model appears to be an important research challenge. In this paper, we propose an approach to design the first-order glitch-extended probing secure TI schemes when quadratic functions are employed in the substitution layer. This method only requires a small amount of fresh random bits and a single clock cycle for its implementation. In particular, the random bits in our approach are reusable and compatible with the changing of the guards technique. Our dedicated TI scheme for the AES cipher gives 20.23% smaller implementation area and 4.2% faster encryption compared to the TI scheme of AES (without using fresh randomness) proposed in CHES 2021. Additionally, we propose a parallel implementation of two S-boxes that further reduces latency (about 39.83%) at the expense of increasing the chip area by 9%. We have positively confirmed the security of AES under the glitch-extended probing model using the verification tool - SILVER and the side-channel leakage assessment method - TVLA.

ePrint: https://eprint.iacr.org/2023/1856

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