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：利用互信息（MI）作为衡量密码系统效率的指标有着悠久的历史。然而，在高维空间中估计未知随机变量之间的MI极具挑战性。机器学习的最新进展使得利用神经网络估计MI成为可能。本文提出了一种将MI估计应用于密码学领域的新方法。我们建议直接将此方法应用于选择明文攻击中，以估计明文与密文之间的MI。加密过程中泄露的任何信息都可能被攻击者利用，从而破坏密码系统的计算安全性。我们通过经验分析多种加密方案和基线方法，评估了所提方法的效率。此外，我们将分析扩展到提供个体安全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。