Using Topological Data Analysis to classify Encrypted Bits

• URL: http://arxiv.org/abs/2301.07393v1
• Date: Wed, 18 Jan 2023 09:43:00 GMT
• Title: Using Topological Data Analysis to classify Encrypted Bits
• Authors: Jayati Kaushik and Aaruni Kaushik and Upasana Parashar
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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