对称布尔函数的算术相关函数

算术相关函数是最近提出的一种研究布尔函数密码学性质的方法,该方法通过定义多元2-adic数上的加法和乘法运算,构建一种新的环结构,实现对经典相关函数的带进位计算的模拟。首先介绍了算术相关函数的定义,并针对具有良好密码学性质的对称布尔函数讨论了其算术相关函数的性质和取值,最后利用对称布尔函数的实值对称性证明了对称布尔函数的算术自相关函数也是一个与向量的重量有关的实值对称函数,至多是n+1值的。     

对称布尔函数的算术相关函数

算术相关函数是最近提出的一种研究布尔函数密码学性质的方法,该方法通过定义多元2-adic数上的加法和乘法运算,构建一种新的环结构,实现对经典相关函数的带进位计算的模拟。首先介绍了算术相关函数的定义,并针对具有良好密码学性质的对称布尔函数讨论了其算术相关函数的性质和取值,最后利用对称布尔函数的实值对称性证明了对称布尔函数的算术自相关函数也是一个与向量的重量有关的实值对称函数,至多是n+1值的。     

互补对称布尔函数的非线性度

互补对称布尔函数是一类特殊的对称布尔函数。在所有代数免疫最优的对称布尔函数中,有相当的比例均属此类函数。特别是当变元数量为2m元时,有2/3比例的代数免疫最优对称布尔函数都是互补对称布尔函数。通过布尔函数非线性度、Walsh谱和Krawtchouk多项式间的关系,计算出互补对称布尔函数的非线性度。结果表明,任意n元互补对称布尔函数的非线性度为2n-1-1/2[nn/2]     

最优代数免疫布尔函数的完全构造

任意的布尔函数可以唯一地表示成有限域上的单变元多项式函数,利用布尔函数的单变元多项式表示和代数编码理论,讨论了布尔函数的代数免疫达到最优的判别条件,得到了布尔函数的变元个数为奇数时,布尔函数具有最优代数免疫(MAI)的等价判别条件。利用该等价判别条件,给出3元布尔函数满足MAI的等价判别条件,进而构造出所有3元的MAI布尔函数。     

非线性布尔函数处理模型研究与设计

针对序列密码算法中非线性布尔函数的实现,利用布尔函数的拆分建模,提出一种以布尔函数拆分、布尔函数计算和输出回写为基础的通用非线性布尔函数处理模型,模型能够完成不同形式的非线性布尔函数实现.为更好地评估模型的实现能力,完成了序列密码中常见的非线性布尔函数的算法适配.适配结果表明,和现有的方式相比,模型适配能力较强,资源占用低,具有较好的发展前景.     

基于引力搜索的布尔函数生成算法

布尔函数是在密码学、纠错编码和扩频通信等领域有着广泛应用的密码函数,寻找性能优良的布尔函数一直是密码学领域的重要问题之一。基于引力搜索算法设计了一种搜索布尔函数的新算法。该算法模仿万有引力定律,以n维空间中的质量点表示布尔函数,以布尔函数的密码特性作为目标适应度函数进行搜索。实验结果表明,算法使用新设计的目标适应度函数可以直接生成具有1阶弹性、1阶扩散准则和高非线性度、高代数次数以及低自相关指标等多种密码学指标的平衡布尔函数,并且进一步给出了直接生成2输出平衡布尔函数的计算机搜索算法。     

三类布尔函数的相关函数研究

布尔函数的相关函数能刻画其扩散特征和线性结构特征,所以研究相关函数的性质对于布尔函数理论具有重要作用。为此,根据自相关和互相关函数的定义,分析通过迹表示的二次布尔函数f(x)=Tr_1~n(x~(2~i+1)+x(2~′+1))的自相关函数值,给出互相关函数平方的一个表达式C_(f,g)~2(α)=(?)(-1)~(D_(f,g)(a)+D_(f,g)(a+ω)),利用该表达式给出任意三次布尔函数的自相关函数平方和的上界,并借助该上界进一步研究两类迹表示的三次布尔函数的绝对值指标上界问题。     

一种改进的非线性布尔函数硬件设计与实现

针对序列密码算法中非线性布尔函数硬件实现资源占用大、结构复杂等问题,深入分析shannon分解定理及布尔函数的操作特征,设计处理布尔函数运算的基本结构—高级可编程逻辑单元。在此基础上提出了高次非线性布尔函数处理架构并对算法进行了适配。架构的性能分析表明,与传统方式相比,该架构对非线性布尔函数具有良好的适配性且资源消耗降低25%。     

布尔函数的复杂系数及其应用

本文利用线性复杂度相关理论，给出了布尔函数复杂系数的定义：得出任何布尔函数的线性复杂度均等于这个函数的复杂系数；给出了一种快速求解布尔函数多项式表示的算法；研究了Bent函数的线性复杂度特点，利用布尔函数的复杂系数，得出布尔函数为Bent函数的一个必要条件。     

复合布尔函数中含某单乘积项的快速判定算法

引入了布尔函数的基本积、单乘积项等概念，通过实例剖析了复合布尔函数中是否含某一单乘积项的一般判定方法，在该基础上提出了一种快速判定途径，并给出了快速判定算法的具体描述，同时对算法进行了实验，结果表明该算法快速有效。     

Dualization, decision lists and identification of monotone discrete functions

Many data-analysis algorithms in machine learning, datamining and a variety of other disciplines essentially operate on discrete multi-attribute data sets. By means of discretisation or binarization also numerical data sets can be successfully analysed. Therefore, in this paper we view/introduce the theory of (partially defined) discrete functions as an important theoretical tool for the analysis of multi-attribute data sets. In particular we study monotone (partially defined) discrete functions. Compared with the theory of Boolean functions relatively little is known about (partially defined) monotone discrete functions. It appears that decision lists are useful for the representation of monotone discrete functions. Since dualization is an important tool in the theory of (monotone) Boolean functions, we study the interpretation and properties of the dual of a (monotone) binary or discrete function. We also introduce the dual of a pseudo-Boolean function. The results are used to investigate extensions of partially defined monotone discrete functions and the identification of monotone discrete functions. In particular, we present a polynomial time algorithm for the identification of so-called stable discrete functions. This revised version was published online in June 2006 with corrections to the Cover Date.     

Boolean functions with long prime implicants

In this short note we introduce a hierarchy of classes of Boolean functions, where each class is defined by the minimum allowed length of prime implicants of the functions in the class. We show that for a given DNF and a given class in the hierarchy, it is possible to test in polynomial time whether the DNF represents a function from the given class. For the first class in the hierarchy we moreover present a polynomial time algorithm which for a given input DNF outputs a shortest logically equivalent DNF, i.e. a shortest DNF representation of the underlying function. This class is therefore a new member of a relatively small family of classes for which the Boolean minimization problem can be solved in polynomial time. For the second class and higher classes in the hierarchy we show that the Boolean minimization problem can be approximated within a constant factor.     

Recognition of interval Boolean functions

Interval functions constitute a special class of Boolean functions for which it is very easy and fast to determine their functional value on a specified input vector. The value of an n-variable interval function specified by interval [a,b] (where a and b are n-bit binary numbers) is true if and only if the input vector viewed as an n-bit number belongs to the interval [a,b]. In this paper we study the problem of deciding whether a given disjunctive normal form represents an interval function and if so then we also want to output the corresponding interval. For general Boolean functions this problem is co-NP-hard. In our article we present a polynomial time algorithm which works for monotone functions. We shall also show that given a Boolean function f belonging to some class which is closed under partial assignment and for which we are able to solve the satisfiability problem in polynomial time, we can also decide whether f is an interval function in polynomial time. We show how to recognize a “renamable” variant of interval functions, i.e., their variable complementation closure. Another studied problem is the problem of finding an interval extension of partially defined Boolean functions. We also study some other properties of interval functions.      

Extracting rules from trained neural networks

Presents an algorithm for extracting rules from trained neural networks. The algorithm is a decompositional approach which can be applied to any neural network whose output function is monotone such as a sigmoid function. Therefore, the algorithm can be applied to multilayer neural networks, recurrent neural networks and so on. It does not depend on training algorithms, and its computational complexity is polynomial. The basic idea is that the units of neural networks are approximated by Boolean functions. But the computational complexity of the approximation is exponential, and so a polynomial algorithm is presented. The author has applied the algorithm to several problems to extract understandable and accurate rules. The paper shows the results for the votes data, mushroom data, and others. The algorithm is extended to the continuous domain, where extracted rules are continuous Boolean functions. Roughly speaking, the representation by continuous Boolean functions means the representation using conjunction, disjunction, direct proportion, and reverse proportion. This paper shows the results for iris data.     

Fast sequential evaluation of monotonic Boolean functions

A correspondence between the factored form representation of monotonic Boolean functions and the sequential evaluation procedures for them is shown to exist. Based on such a relationship, a criterion is developed for obtaining the cost of the sequential procedure directly from the factored form representation. Making use of this criterion, a simple algorithm is presented to determine the fast sequential evaluation procedure for monotonic Boolean functions.     

Decision tree approximations of Boolean functions

Decision trees are popular representations of Boolean functions. We show that, given an alternative representation of a Boolean function f, say as a read-once branching program, one can find a decision tree T which approximates f to any desired amount of accuracy. Moreover, the size of the decision tree is at most that of the smallest decision tree which can represent f and this construction can be obtained in quasi-polynomial time. We also extend this result to the case where one has access only to a source of random evaluations of the Boolean function f instead of a complete representation. In this case, we show that a similar approximation can be obtained with any specified amount of confidence (as opposed to the absolute certainty of the former case.) This latter result implies proper PAC-learnability of decision trees under the uniform distribution without using membership queries.     

奇数变元plateaued函数代数免疫性质研究

Plateaued函数是包含Bent函数和部分Bent函数的更大函数类,具有许多优良的密码学性质。基于布尔函数非线性度与代数免疫阶之间的关系,利用Walsh谱等工具,讨论奇数变元的plateaued函数的代数免疫性质,得到其存在低次零化子的一个充分条件,并进一步刻画变元个数n与plateaued函数的阶r之间的具体关系,利用此关系可确定函数代数免疫阶的上界。     

Generation of sign Walsh spectra from disjoint cubes of Boolean functions

A new algorithm is given that converts a reduced representation of Boolean functions in the form of disjoint cubes to sign Walsh spectra. Since the known algorithms that generate sign Walsh spectra always start from the truth table of Boolean functions, the method presented computes faster with a smaller computer memory. The method is especially efficient for such Boolean functions that are described by only few disjoint cubes.