  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 and
transformed data, thereby enhancing privacy. Our experimental results confirm
that these approaches achieve superior privacy protection and retain high
utility, making them viable for practical applications where both aspects are
crucial. The research contributes to the field by providing a flexible and
effective strategy for deploying privacy-preserving algorithms across various
data types and establishing new benchmarks for utility and confidentiality in
data analytics.
