# Reviving Purpose Limitation and Data Minimisation in Data-Driven Systems

arXiv: https://arxiv.org/abs/2101.06203 · HTML: https://mlsanity.com/paper/2101.06203/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 15 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Certified Data Removal from Machine Learning Models](https://mlsanity.com/paper/1911.03030/) (2019)
- [Formalizing Data Deletion in the Context of the Right to be Forgotten](https://mlsanity.com/paper/2002.10635/) (2020)

## Built on by (most cited first, 11 of 11)

- [On the Trade-Off between Actionable Explanations and the Right to be Forgotten](https://mlsanity.com/paper/2208.14137/) (2022)
- [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)
- [The Data Minimization Principle in Machine Learning](https://mlsanity.com/paper/2405.19471/) (2024)
- [The Right to be Forgotten in Pruning: Unveil Machine Unlearning on Sparse Models](https://mlsanity.com/paper/2507.18725/) (2025)
- [Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data](https://mlsanity.com/paper/2503.01630/) (2025)
- [From Principle to Practice: Vertical Data Minimization for Machine Learning](https://mlsanity.com/paper/2311.10500/) (2023)
- [Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models](https://mlsanity.com/paper/2504.16969/) (2025)
- [Efficient Knowledge Graph Unlearning with Zeroth-order Information](https://mlsanity.com/paper/2508.14013/) (2025)
- [Position: EU AI Act's Research Exemptions Can Break the Publication Norms of Major AI Conferences](https://mlsanity.com/paper/2506.03218/) (2025)
- [What Data is Really Necessary? A Feasibility Study of Inference Data Minimization for Recommender Systems](https://mlsanity.com/paper/2508.21547/) (2025)
- [Only Whats Necessary: Pareto Optimal Data Minimization for Privacy Preserving Video Anomaly Detection](https://mlsanity.com/paper/2603.26354/) (2026)

## Abstract (arXiv)

This paper determines whether the two core data protection principles of data minimisation and purpose limitation can be meaningfully implemented in data-driven systems. While contemporary data processing practices appear to stand at odds with these principles, we demonstrate that systems could technically use much less data than they currently do. This observation is a starting point for our detailed techno-legal analysis uncovering obstacles that stand in the way of meaningful implementation and compliance as well as exemplifying unexpected trade-offs which emerge where data protection law is applied in practice. Our analysis seeks to inform debates about the impact of data protection on the development of artificial intelligence in the European Union, offering practical action points for data controllers, regulators, and researchers.