Reviving Purpose Limitation and Data Minimisation in Data-Driven Systems
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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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 2 of its 10 references that are in the map. First posted on arXiv 15 Jan 2021.
Builds on
2 of 10 references.
References · 2
- 2019Certified Data Removal from Machine Learning Modelscited by 512
- 2020Formalizing Data Deletion in the Context of the Right to be Forgottencited by 46
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 6; 2026: 1.
11 papers.
- 2022On the Trade-Off between Actionable Explanations and the Right to be Forgottencited by 20
- 2021Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimizationcited by 9
- 2024The Data Minimization Principle in Machine Learningcited by 7
- 2025The Right to be Forgotten in Pruning: Unveil Machine Unlearning on Sparse Modelscited by 5
- 2025Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Datacited by 4
- 2023From Principle to Practice: Vertical Data Minimization for Machine Learningcited by 2
- 2025Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Modelscited by 2
- 2025Efficient Knowledge Graph Unlearning with Zeroth-order Informationcited by 2
- 2025Position: EU AI Act's Research Exemptions Can Break the Publication Norms of Major AI Conferencescited by 0
- 2025What Data is Really Necessary? A Feasibility Study of Inference Data Minimization for Recommender Systemscited by 0
- 2026Only Whats Necessary: Pareto Optimal Data Minimization for Privacy Preserving Video Anomaly Detectioncited by 0
- 2026Only Whats Necessary: Pareto Optimal Data Minimization for Privacy Preserving Video Anomaly Detection27 Mar 2026
- 2025What Data is Really Necessary? A Feasibility Study of Inference Data Minimization for Recommender Systems29 Aug 2025
- 2025Efficient Knowledge Graph Unlearning with Zeroth-order Information19 Aug 2025
- 2025The Right to be Forgotten in Pruning: Unveil Machine Unlearning on Sparse Models24 Jul 2025
- 2025Position: EU AI Act's Research Exemptions Can Break the Publication Norms of Major AI Conferences3 Jun 2025
- 2025Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models23 Apr 2025
- 2025Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data3 Mar 2025
- 2024The Data Minimization Principle in Machine Learning29 May 2024
- 2023From Principle to Practice: Vertical Data Minimization for Machine Learning17 Nov 2023
- 2022On the Trade-Off between Actionable Explanations and the Right to be Forgotten30 Aug 2022
- 2021Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization16 Jul 2021