Synergizing Privacy and Utility in Data Analytics Through Advanced Information Theorization

arXiv:2404.16241v1 Announce Type: new
Abstract: This study develops a novel framework for privacy-preserving data analytics, addressing the critical challenge of balancing data utility with privacy concerns. We introduce three sophisticated algorithms: a Noise-Infusion Technique tailored for high-dimensional image data, a Variational Autoencoder (VAE) for robust feature extraction while masking sensitive attributes and an Expectation Maximization (EM) approach optimized for structured data privacy. Applied to datasets such as Modified MNIST and CelebrityA, our methods significantly reduce mutual information between sensitive attributes …

advanced algorithms analytics arxiv challenge critical cs.cr data data analytics extraction feature framework high image information masking noise novel privacy privacy concerns study utility