  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.
