mlsanityData to 7 Oct 2026

Reviving Purpose Limitation and Data Minimisation in Data-Driven Systems

arXiv 2101.06203 ↗First posted 15 Jan 2021cs.CY cs.IR cs.LGcited by 11

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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

  1. 2019Certified Data Removal from Machine Learning Modelscited by 512
  2. 2020Formalizing Data Deletion in the Context of the Right to be Forgottencited by 46

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 6; 2026: 1.

11 papers.