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A Novel AES Random Mask Scheme Against Correlation Power Analysis

  • Ge JiaoEmail author
  • Lang Li
  • Yi Zou
Conference paper
  • 4 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1143)

Abstract

With the wide application of smart card, people’s demand for the security of smart card is increasing. The Advanced Encryption Standard (AES) algorithm in smart card itself is safe enough, but the encryption algorithm is still threatened by side-channel attacks due to time, power consumption, electromagnetic radiation, and other information leakage during operation. Aiming at the shortcomings of existing mask schemes in security, a smart card AES encryption NARMS mask against side-channel attack is proposed. This scheme calculates the random mask according to the random hamming weight value and random integer, selects three rounds of random selection and NARMS random mask strategy for protection in the first, second, and ninth rounds of AES algorithm, optimizes and improves the design of the random mask and the overall process of mask protection. The mask scheme designed in this paper is comprehensively compared with ordinary AES algorithm, fixed value mask scheme and rotating S-box masking scheme (RSM). Experiments show that this scheme can effectively resist the first- and second-order correlation power analysis (CPA) attacks and has high efficiency, thus ensuring the key security of AES algorithm and privacy security of smart card application.

Keywords

Random mask Advanced encryption standard Correlation power analysis Hamming weight 

Notes

Acknowledgements

This work is supported by the Science and Technology Plan Project of Hunan Province [2016TP1020], the Application-oriented Special Disciplines, Double First-Class University Project of Hunan Province [Xianjiaotong[2018] 469], the Hunan Province Special Funds of Central Government for Guiding Local Science and Technology Development [2018CT5001], the Subject Group Construction Project of Hengyang Normal University [18XKQ02].

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Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyHengyang Normal UniversityHengyangChina
  2. 2.Hunan Provincial Key Laboratory of Intelligent Information Processing and ApplicationHengyangChina

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