The use of Mutual Information (MI) as a measure to evaluate the efficiency of cryptosystems has an extensive history. However, estimating MI between unknown random variables in a high-dimensional space is challenging. Recent advances in machine learning have enabled progress in estimating MI using neural networks. This work presents a novel application of MI estimation in the field of cryptography. We propose applying this methodology directly to estimate the MI between plaintext and ciphertext in a chosen plaintext attack. The leaked information, if any, from the encryption could potentially be exploited by adversaries to compromise the computational security of the cryptosystem. We evaluate the efficiency of our approach by empirically analyzing multiple encryption schemes and baseline approaches. Furthermore, we extend the analysis to novel network coding-based cryptosystems that provide individual secrecy and study the relationship between information leakage and input distribution.

翻译：使用互信息（MI）作为衡量密码系统效率的指标已有悠久历史。然而，在高维空间中估计未知随机变量间的互信息极具挑战性。机器学习的最新进展使得利用神经网络估计互信息成为可能。本文提出了互信息估计在密码学领域的新应用。我们建议直接运用该方法在已知明文攻击场景下估计明文与密文间的互信息。若加密过程存在信息泄露，攻击者可能利用这些信息破坏密码系统的计算安全性。通过对多种加密方案及基线方法的实证分析，我们评估了所提方法的有效性。此外，我们将分析扩展至具有个体保密性的新型网络编码密码系统，并研究了信息泄露与输入分布之间的关系。