Utility Privacy Trade

Utility-privacy trade-off research focuses on developing methods for using sensitive data in machine learning while minimizing privacy risks. Current efforts concentrate on improving the efficiency and effectiveness of privacy-preserving techniques like differential privacy, often employing autoencoders, adversarial networks, and Bayesian privacy frameworks to achieve optimal balances between data utility and privacy guarantees. This research is crucial for enabling responsible data usage in various applications, particularly in sensitive domains like healthcare and finance, by providing a rigorous framework for quantifying and managing the inherent tension between data utility and individual privacy. The development of robust and efficient methods for achieving this balance is vital for fostering trust and ethical considerations in data-driven applications.