Boolean functions are important primitives in different domains of cryptology, complexity and coding theory. In this paper, we connect the tools from cryptology and complexity theory in the domain of Boolean functions with low polynomial degree and high sensitivity. It is well known that the polynomial degree of of a Boolean function and its resiliency are directly connected. Using this connection we analyze the polynomial degree-sensitivity values through the lens of resiliency, demonstrating existence and non-existence results of functions with low polynomial degree and high sensitivity on small number of variables (upto 10). In this process, borrowing an idea from complexity theory, we show that one can implement resilient Boolean functions on a large number of variables with linear size and logarithmic depth. Finally, we extend the notion of sensitivity to higher order and note that the existing construction idea of Nisan and Szegedy (1994) can provide only constant higher order sensitivity when aiming for polynomial degree of $n-\omega(1)$. In this direction, we present a construction with low ($n-\omega(1)$) polynomial degree and super-constant $\omega(1)$ order sensitivity exploiting Maiorana-McFarland constructions, that we borrow from construction of resilient functions. The questions we raise identify novel combinatorial problems in the domain of Boolean functions.
翻译:布尔函数是不同密码学、 复杂和编码理论领域的重要原始元素。 在本文中, 我们将布尔函数领域的密码学和复杂理论工具与低多元度和高敏感度联系起来。 众所周知, 布林函数及其再适应性的多边程度是直接相连的。 使用此联系, 我们通过弹性透镜分析多感度感知值, 显示多感度低的功能的存在和不存在结果, 对少量变量( 至 10) 高度敏感。 在此过程中, 我们借用了复杂理论的理念, 我们显示, 可以在大量具有线性大小和对数深度的变量上执行具有弹性的布林函数。 最后, 我们把敏感性的概念扩大到更高的顺序, 并注意, 尼桑 和 Szegedy (1994年) 的现有构建理念只有在以 $-\ omega $ 的多元度为目标时, 并且对少量变量( 至 10 10 ) 的多感知性理论中, 我们展示了以低( $- omga(1)) 度的) 智能智能智能智能构建功能来进行弹性的布林度的布林多米纳( ) 库纳( ) 度( AS级) ) 度( ) 度的智能 的智能 的智能 度( ) 质质质质变数号) 。