  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.

Cryptography and Security;Machine Learning