Advancing Robust and Ethical Data Minimization Techniques: Theoretical Foundations and Practical Implementations

Abstract

With the proliferation of big data and advanced analytics, there are growing concerns about privacy, transparency, and ethics. Data minimization has emerged as an important principle to address these issues by collecting, processing, and storing only essential data. However, practical implementations of data minimization pose significant technical and ethical challenges. This paper provides a comprehensive review of the theoretical foundations and state-of-the-art techniques for robust and ethical data minimization. We survey diverse methods from statistics, machine learning, security, and privacy that enable minimizing data while preserving utility. We highlight emerging directions, such as federated learning and differential privacy, that limit data exposure. For real-world deployments, we discuss trust, transparency, and accountability requirements. Our analysis outlines important open problems in rectifying tensions between innovation and ethics. We also propose a unifying framework to advance research on aligning the dual goals of minimizing data and maximizing benefits. Through technical and ethical perspectives, our work serves as a roadmap for developing principled data minimization techniques with rigorous privacy and utility guarantees.