The Data Minimization Principle in Machine Learning

TL;DR

This paper introduces an optimization framework for implementing data minimization in machine learning, aligning legal principles with practical algorithms to enhance privacy while acknowledging real-world privacy complexities.

Contribution

It provides a rigorous formulation of data minimization based on legal definitions and adapts optimization algorithms for practical implementation.

Findings

Optimization algorithms can effectively reduce data collection while maintaining model performance.

Current privacy benefits of data minimization may be overestimated without considering real-world privacy risks.

The framework highlights gaps between legal privacy expectations and actual privacy outcomes.

Abstract

The principle of data minimization aims to reduce the amount of data collected, processed or retained to minimize the potential for misuse, unauthorized access, or data breaches. Rooted in privacy-by-design principles, data minimization has been endorsed by various global data protection regulations. However, its practical implementation remains a challenge due to the lack of a rigorous formulation. This paper addresses this gap and introduces an optimization framework for data minimization based on its legal definitions. It then adapts several optimization algorithms to perform data minimization and conducts a comprehensive evaluation in terms of their compliance with minimization objectives as well as their impact on user privacy. Our analysis underscores the mismatch between the privacy expectations of data minimization and the actual privacy benefits, emphasizing the need for approaches that account for multiple facets of real-world privacy risks.