One Truth Prevails: A Deep-learning Based Single-Trace Power Analysis on RSA–CRT with Windowed Exponentiation
نویسندگان
چکیده
In this paper, a deep-learning based power/EM analysis attack on the state-of-the-art RSA–CRT software implementation is proposed. Our method applied to side-channel-aware with Gnu Multi-Precision (MP) Library, which typical open-source library. MP employs fixed-window exponentiation, fastest in constant time, and loads entire precomputation table once avoid side-channel leaks from multiplicands. To conduct an accurate estimation of secret exponents, our focuses process loading table, we call dummy load scheme. It particularly noteworthy that scheme implemented as countermeasure against simple (SPA/SEMA). This type vulnerability also exists other cryptographic libraries. We propose partial key exposure suitable for distribution errors inthe exponents recovered windowed exponentiation. experimentally show proposed consisting above attack, well can be used fully recover information single decryption or signature process.
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ژورنال
عنوان ژورنال: IACR transactions on cryptographic hardware and embedded systems
سال: 2022
ISSN: ['2569-2925']
DOI: https://doi.org/10.46586/tches.v2022.i4.490-526