CRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation

TL;DR

This paper introduces a neural network-based method to estimate mutual information between plaintext and ciphertext, enabling analysis of information leakage in cryptosystems and assessing their security vulnerabilities.

Contribution

It presents a novel application of mutual information neural estimation to cryptanalysis, including evaluation on various encryption schemes and new network coding-based cryptosystems.

Findings

Neural MI estimation effectively detects information leakage.

Analysis reveals vulnerabilities in certain cryptosystems.

Extended to novel cryptosystems with input distribution considerations.

Abstract

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.