SoK: Data Minimization in Machine Learning

TL;DR

This paper provides a comprehensive overview of data minimization in machine learning, emphasizing its importance for privacy and regulation compliance, and introduces a unified framework to guide research and practice in this area.

Contribution

It presents the first systematic analysis of data minimization in ML, offering a unified framework and clarifying terminology, metrics, and trade-offs for practitioners and researchers.

Findings

Introduces a general framework for DMML including data pipeline and adversarial models.

Systematically reviews existing DMML literature and related methodologies.

Helps practitioners identify relevant techniques and understand assumptions in DMML.

Abstract

Data minimization (DM) describes the principle of collecting only the data strictly necessary for a given task. It is a foundational principle across major data protection regulations like GDPR and CPRA. Violations of this principle have substantial real-world consequences, with regulatory actions resulting in fines reaching hundreds of millions of dollars. Notably, the relevance of data minimization is particularly pronounced in machine learning (ML) applications, which typically rely on large datasets, resulting in an emerging research area known as Data Minimization in Machine Learning (DMML). At the same time, existing work on other ML privacy and security topics often addresses concerns relevant to DMML without explicitly acknowledging the connection. This disconnect leads to confusion among practitioners, complicating their efforts to implement DM principles and interpret the terminology, metrics, and evaluation criteria used across different research communities. To address this gap, we present the first systematization of knowledge (SoK) for DMML. We introduce a general framework for DMML, encompassing a unified data pipeline, adversarial models, and points of minimization. This framework allows us to systematically review data minimization literature as well as DM-adjacent methodologies whose link to DM was often overlooked. Our structured overview is designed to help practitioners and researchers effectively adopt and apply DM principles in ML, by helping them identify relevant techniques and understand underlying assumptions and trade-offs through a DM-centric lens.