# Only Whats Necessary: Pareto Optimal Data Minimization for Privacy Preserving Video Anomaly Detection

arXiv: https://arxiv.org/abs/2603.26354 · HTML: https://mlsanity.com/paper/2603.26354/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Abnormal Event Detection in Videos using Spatiotemporal Autoencoder](https://mlsanity.com/paper/1701.01546/) (2017)
- [SPAct: Self-supervised Privacy Preservation for Action Recognition](https://mlsanity.com/paper/2203.15205/) (2022)
- [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)
- [The Data Minimization Principle in Machine Learning](https://mlsanity.com/paper/2405.19471/) (2024)
- [Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation](https://mlsanity.com/paper/2204.00279/) (2022)
- [Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization](https://mlsanity.com/paper/2504.14301/) (2025)
- [I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data](https://mlsanity.com/paper/2210.13954/) (2022)

## Abstract (arXiv)

Video anomaly detection (VAD) systems are increasingly deployed in safety critical environments and require a large amount of data for accurate detection. However, such data may contain personally identifiable information (PII), including facial cues and sensitive demographic attributes, creating compliance challenges under the EU General Data Protection Regulation (GDPR). In particular, GDPR requires that personal data be limited to what is strictly necessary for a specified processing purpose. To address this, we introduce Only What's Necessary, a privacy-by-design framework for VAD that explicitly controls the amount and type of visual information exposed to the detection pipeline. The framework combines breadth based and depth based data minimization mechanisms to suppress PII while preserving cues relevant to anomaly detection. We evaluate a range of minimization configurations by feeding the minimized videos to both a VAD model and a privacy inference model. We employ two ranking based methods, along with Pareto analysis, to characterize the resulting trade off between privacy and utility. From the non-dominated frontier, we identify sweet spot operating points that minimize personal data exposure with limited degradation in detection performance. Extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed framework.