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CRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation

DOI:
10.60864/a2yc-7f51
Citation Author(s):
Benjamin D. Kim, Vipindev Adat Vasudevan, Jongchan Woo, Alejandro Cohen, Rafael G. L. D'Oliveira, Thomas Stahlbuhk, Muriel Médard
Submitted by:
Vipindev Adat V...
Last updated:
6 June 2024 - 10:50am
Document Type:
Poster
Document Year:
2024
Event:
Presenters:
Vipindev Adat Vasudevan
Paper Code:
IFS-P6.6
 

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.

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