# Using Topological Data Analysis to classify Encrypted Bits

arXiv: https://arxiv.org/abs/2301.07393 · HTML: https://mlsanity.com/paper/2301.07393/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 18 Jan 2023.

## Builds on (most cited first, 1 of 1)

- [A Topological "Reading" Lesson: Classification of MNIST using TDA](https://mlsanity.com/paper/1910.08345/) (2019)

## Abstract (arXiv)

We present a way to apply topological data analysis for classifying encrypted bits into distinct classes. Persistent homology is applied to generate topological features of a point cloud obtained from sets of encryptions. We see that this machine learning pipeline is able to classify our data successfully where classical models of machine learning fail to perform the task. We also see that this pipeline works as a dimensionality reduction method making this approach to classify encrypted data a realistic method to classify the given encryptioned bits.