On the security of multivariate hash functions

Multivariate hash functions are a type of hash functions whose compression function is explicitly defined as a sequence of multivariate equations. Billet et al designed the hash function MQ-HASH and Ding et al proposed a similar construction. In this paper, we analyze the security of multivariate hash functions and conclude that low degree multivariate functions such as MQ-HASH are neither pseudo-random nor unpredictable. There may be trivial collisions and fixed point attacks if the parameters of the compression ftmction have been chosen. And they are also not computation-resistance, which makes MAC forgery easily.     

大整数模幂算法研究进展及其探讨

大整数模幂运算在公钥密码算法运算效率中起着决定性的作用.RSA算法加密与解密都使用了模幂运算.对于加密,要计算密文C=Memod N,对于解密,要计算明文M=Cd mod N,其中e和d分别为公钥和私钥.当e和d非常大的时候(可能达到几百位或者上千位)时,计算速度就会大大降低.因此,研究快速幂的运算效率显得非常重要.本文就是研究计算y=gsmod m的效率问题,论述了大整数模幂算法在国际上最新的进展,并实现相关的算法,进行测试比较,并给出结论.     

Integration of learning algorithm on fuzzy min-max neural networks

An integrated fuzzy min-max neural network (IFMMNN) is developed to avoid the classification result influenced by the input sequence of training samples, and the learning algorithm can be used as pure clustering, pure classification, or a hybrid clustering classification. Three experiments are designed to realize the aim. The serial input of samples is changed to parallel input, and the fuzzy membership function is substituted by similarity matrix. The experimental results show its superiority in contrast with the original method proposed by Simpson.