# The Data Minimization Principle in Machine Learning

arXiv: https://arxiv.org/abs/2405.19471 · HTML: https://mlsanity.com/paper/2405.19471/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Deep Learning on a Data Diet: Finding Important Examples Early in Training](https://mlsanity.com/paper/2107.07075/) (2021)
- [Beyond neural scaling laws: beating power law scaling via data pruning](https://mlsanity.com/paper/2206.14486/) (2022)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [A Survey of Privacy Attacks in Machine Learning](https://mlsanity.com/paper/2007.07646/) (2020)
- [Low-Cost High-Power Membership Inference Attacks](https://mlsanity.com/paper/2312.03262/) (2023)
- [Data Minimization for GDPR Compliance in Machine Learning Models](https://mlsanity.com/paper/2008.04113/) (2020)
- [Operationalizing the Legal Principle of Data Minimization for Personalization](https://mlsanity.com/paper/2005.13718/) (2020)
- [Reviving Purpose Limitation and Data Minimisation in Data-Driven Systems](https://mlsanity.com/paper/2101.06203/) (2021)
- [Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization](https://mlsanity.com/paper/2107.08096/) (2021)
- [Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation](https://mlsanity.com/paper/2204.00279/) (2022)
- [Participatory Personalization in Classification](https://mlsanity.com/paper/2302.03874/) (2023)
- [Finding Optimal Strategies in a Multi-Period Multi-Leader-Follower Stackelberg Game Using an Evolutionary Algorithm](https://mlsanity.com/paper/1307.6246/) (2013)
- [From Principle to Practice: Vertical Data Minimization for Machine Learning](https://mlsanity.com/paper/2311.10500/) (2023)
- [Personalized Privacy Auditing and Optimization at Test Time](https://mlsanity.com/paper/2302.00077/) (2023)

## Built on by (most cited first, 7 of 7)

- [Protecting Users From Themselves: Safeguarding Contextual Privacy in Interactions with Conversational Agents](https://mlsanity.com/paper/2502.18509/) (2025)
- [Data Lineage Inference: Uncovering Privacy Vulnerabilities of Dataset Pruning](https://mlsanity.com/paper/2411.15796/) (2024)
- [SoK: Data Minimization in Machine Learning](https://mlsanity.com/paper/2508.10836/) (2025)
- [Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams](https://mlsanity.com/paper/2503.05675/) (2025)
- [Metanetworks as Regulatory Operators: Learning to Edit for Requirement Compliance](https://mlsanity.com/paper/2512.15469/) (2025)
- [From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Management](https://mlsanity.com/paper/2602.05016/) (2026)
- [Only Whats Necessary: Pareto Optimal Data Minimization for Privacy Preserving Video Anomaly Detection](https://mlsanity.com/paper/2603.26354/) (2026)

## Abstract (arXiv)

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.