Balancing Privacy and Utility: Insights from Information Theory and Differential Privacy

Abstract

Balancing privacy and utility presents a complex challenge in the age of information technology, where data is increasingly abundant and valuable. Drawing insights from information theory and the principles of differential privacy offers a nuanced approach to this dilemma. Information theory provides a framework for quantifying the amount of information leaked in data releases, enabling the assessment of privacy risks. On the other hand, the concept of differential privacy offers a rigorous mathematical definition of privacy guarantees, ensuring that the inclusion or exclusion of any individual's data does not significantly affect the outcome of the analysis. By integrating these perspectives, we can design data-driven systems that strike a delicate balance between preserving individual privacy and maximizing utility, thus fostering trust and innovation in data-driven applications.