# CRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation

arXiv: https://arxiv.org/abs/2309.08019 · HTML: https://mlsanity.com/paper/2309.08019/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Variational Leakage: The Role of Information Complexity in Privacy Leakage](https://mlsanity.com/paper/2106.02818/) (2021)
- [Network Coding-Based Post-Quantum Cryptography](https://mlsanity.com/paper/2009.01931/) (2020)
- [Partial Encryption after Encoding for Security and Reliability in Data Systems](https://mlsanity.com/paper/2202.03002/) (2022)
- [CERMET: Coding for Energy Reduction with Multiple Encryption Techniques -- It's easy being green](https://mlsanity.com/paper/2308.05063/) (2023)

## Built on by (most cited first, 3 of 3)

- [TexShape: Information Theoretic Sentence Embedding for Language Models](https://mlsanity.com/paper/2402.05132/) (2024)
- [Image selective encryption analysis using mutual information in CNN based embedding space](https://mlsanity.com/paper/2508.08832/) (2025)
- [Optimal Guarantees for Auditing R'enyi Differentially Private Machine Learning](https://mlsanity.com/paper/2605.21938/) (2026)

## Abstract (arXiv)

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.