Optimizing Data Utility and Privacy through Noise-Infused Representation Learning

This study develops a novel framework that effectively balances data utility maximization and privacy preservation through the integration of sophisticated algorithms, including a Noise-Infusion Technique, Variational Autoencoder (VAE), and Expectation Maximization (EM) approach.