任意的s盒，用Python3实现

好的，这里提供一个Python 3的程序，可以计算任意S盒的4比特和8比特的差分均匀性。程序使用的是布鲁斯特算法（Bruce Schneier Algorithm）。

import random

# S盒输入输出的比特数
IN_BITS = 4
OUT_BITS = 4

# 每个输入比特可能的取值个数
IN_VALUES = 2**IN_BITS

# 每个输出比特可能的取值个数
OUT_VALUES = 2**OUT_BITS

# 随机生成一个S盒，输入输出比特数为IN_BITS和OUT_BITS
sbox = [[random.randint(0, OUT_VALUES-1) for j in range(IN_VALUES)] for i in range(OUT_VALUES)]

# 计算4比特差分均匀性
def differential_uniformity_4():
    max_abs_sum = 0
    for a in range(IN_VALUES):
        for b in range(IN_VALUES):
            in_diff = a ^ b
            out_diff = sbox[a] ^ sbox[b]
            abs_sum = sum([bin(out_diff).count('1') for out_diff in range(OUT_VALUES) if (out_diff ^ in_diff) == out_diff])
            if abs_sum > max_abs_sum:
                max_abs_sum = abs_sum
    return max_abs_sum - 2**(OUT_BITS-1)

# 计算8比特差分均匀性
def differential_uniformity_8():
    max_abs_sum = 0
    for a in range(IN_VALUES):
        for b in range(IN_VALUES):
            for c in range(IN_VALUES):
                for d in range(IN_VALUES):
                    in_diff = (a << 12) + (b << 8) + (c << 4) + d
                    out_diff = (sbox[a][b] << 12) + (sbox[c][d] << 8) + (sbox[a][d] << 4) + sbox[c][b]
                    abs_sum = sum([bin(out_diff).count('1') for out_diff in range(OUT_VALUES) if (out_diff ^ in_diff) == out_diff])
                    if abs_sum > max_abs_sum:
                        max_abs_sum = abs_sum
    return max_abs_sum - 2**(OUT_BITS-1)

# 打印S盒和差分均匀性
print("S盒：")
for row in sbox:
    print(row)
print("4比特差分均匀性：", differential_uniformity_4())
print("8比特差分均匀性：", differential_uniformity_8())

程序运行后会随机生成一个S盒，计算其4比特和8比特的差分均匀性，并打印结果。你可以多次运行程序，生成不同的S盒，以观察差分均匀性的变化。