秘密共享:高阶掩码S盒和有限域安全乘法设计
Secret Sharing:Design of Higher-Order Masking S-box and Secure Multiplication in Galois Field
唐啸霖 1冯燕 2李明达 1李志强2
作者信息
- 1. 集成电路制造技术重点实验室(中国科学院) 北京 100029;中国科学院微电子研究所 北京 100029;中国科学院大学 北京 100049
- 2. 集成电路制造技术重点实验室(中国科学院) 北京 100029;中国科学院微电子研究所 北京 100029
- 折叠
摘要
在信息时代,信息安全是最不能忽视的重要问题,对密码设备的攻击和防护是该领域的研究热点.近年来,多种对密码设备的攻击已为人所知,其目的都是为了获取设备中的密钥,在众多攻击中,功耗侧信道攻击是最受关注的攻击技术之一.掩码技术是对抗功耗侧信道攻击的有效方法,然而随着攻击手段的不断进步,1阶掩码的防护已经不足以应对2阶及以上的功耗分析攻击,因此对高阶掩码的研究具有重要的意义.为了提升加密电路抗攻击能力,该文基于秘密共享的思想,对分组密码算法的S盒变换实施了高阶掩码防护——共享型掩码,并基于Ishai等人在Crypto 2003上发表的安全方案(ISW框架)提出了有限域安全乘法的通用设计方法.通过实验表明,该文提出的共享型掩码方案不影响加密算法的功能,同时能抵御1阶和2阶相关功耗分析攻击.
Abstract
In the information era,information security is the priority that cannot be ignored.Attacks and protection against password devices are research hotspots in this field.In recent years,various attacks on cryptographic devices have become well-known,all aimed at obtaining keys from the device.Among these attacks,power side channel attack is one of the most concerned attack techniques.Mask technology is an effective method to combat power side channel attacks,however,with the continuous progress of attack methods,the protection of first-order mask is no longer sufficient to cope with second-order and higher order power analysis attack,so the research on higher-order mask has considerable significance.To enhance the encryption circuit's capability of anti-attack,high-order masking schemes:N-share masking is implemented on S-box in this paper,and a universal design method for galois field secure multiplication is proposed,which is based on the secure scheme published by Ishai et al.at Crypto 2003(ISW framework).Through experiments,it has been shown that the encryption scheme adopted in this paper does not affect the functionality of the encryption algorithm,and can resist first-order and second-order correlation power analysis attack.
关键词
分组密码算法/S盒变换/共享型掩码/有限域乘法Key words
Block cipher algorithm/S-box/N-share mask/Galois field multiplication引用本文复制引用
基金项目
国家重点研发计划(2020YFB2104601)
出版年
2024
NETL
NSTL
万方数据