Efficient collision attacks on smart card implementations of masked AES

Abstract

Collision attack is often employed against some cryptographic algorithms such as AES and DES. As a usual countermeasure, masking can resist such attacks to some extent. In CHES 2011, Clavier et al. proposed a collision-correlation attack based on Pearson correlation coefficient against masking. In this paper, a collision distinguisher based on least absolute deviation against masking is proposed. Subsequently, we suggest three other distinguishers based on least square method, least exponent method, and central moment product, respectively. Our experiments and simulations show that in practice, our distinguishers based on least absolute deviation and least square method perform much better than collision-correlation attack and other proposed distinguishers in this paper. We also give four application examples, which show that even if the masks are not reused, new distinguishers are competent to collision attacks.

摘要

创新点

本文首先给出了基于最小一乘法的碰撞区分器, 并证明了其正确性。 随后讨论了基于最小二乘法、 最小n乘法、 以及中心矩乘积等其它三种有效的模型。 实验表明, 基于最小一乘法和最小二乘法的效果优于其它两种模型和Clavier等人的碰撞相关攻击。 最后, 给出了新方法的四种应用实例, 并讨论了其普适性。