Efficient collision attacks on smart card implementations of masked AES

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.