# Data Minimization for GDPR Compliance in Machine Learning Models

arXiv: https://arxiv.org/abs/2008.04113 · HTML: https://mlsanity.com/paper/2008.04113/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 21 of 21)

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- [Scamming the Scammers: Using ChatGPT to Reply Mails for Wasting Time and Resources](https://mlsanity.com/paper/2303.13521/) (2023)
- [The Data Minimization Principle in Machine Learning](https://mlsanity.com/paper/2405.19471/) (2024)
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- [Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysis](https://mlsanity.com/paper/2310.14312/) (2023)
- [MLOps Spanning Whole Machine Learning Life Cycle: A Survey](https://mlsanity.com/paper/2304.07296/) (2023)
- [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)
- [On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms](https://mlsanity.com/paper/2310.15848/) (2023)
- [Anonymous Bandits for Multi-User Systems](https://mlsanity.com/paper/2210.12198/) (2022)
- [I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data](https://mlsanity.com/paper/2210.13954/) (2022)
- [SoK: Data Minimization in Machine Learning](https://mlsanity.com/paper/2508.10836/) (2025)
- [Vehicle Detection and Classification without Residual Calculation: Accelerating HEVC Image Decoding with Random Perturbation Injection](https://mlsanity.com/paper/2305.08265/) (2023)
- [Towards Sybil Resilience in Decentralized Learning](https://mlsanity.com/paper/2306.15044/) (2023)
- [The trade-off between data minimization and fairness in collaborative filtering](https://mlsanity.com/paper/2410.07182/) (2024)
- [Metanetworks as Regulatory Operators: Learning to Edit for Requirement Compliance](https://mlsanity.com/paper/2512.15469/) (2025)
- [Smoothing the Black-Box: Signed-Distance Supervision for Black-Box Model Copying](https://mlsanity.com/paper/2601.20773/) (2026)
- [Only Whats Necessary: Pareto Optimal Data Minimization for Privacy Preserving Video Anomaly Detection](https://mlsanity.com/paper/2603.26354/) (2026)

## Abstract (arXiv)

The EU General Data Protection Regulation (GDPR) mandates the principle of data minimization, which requires that only data necessary to fulfill a certain purpose be collected. However, it can often be difficult to determine the minimal amount of data required, especially in complex machine learning models such as neural networks. We present a first-of-a-kind method to reduce the amount of personal data needed to perform predictions with a machine learning model, by removing or generalizing some of the input features. Our method makes use of the knowledge encoded within the model to produce a generalization that has little to no impact on its accuracy. This enables the creators and users of machine learning models to acheive data minimization, in a provable manner.