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

2026-03-27

AI Summary

This work addresses the challenge of complying with GDPR’s data minimization principle in video anomaly detection, where personal identifiable information often complicates privacy compliance. To this end, the authors propose a privacy-first design framework that, for the first time, introduces Pareto optimality into this domain. The approach integrates a breadth-and-depth data minimization mechanism that effectively suppresses sensitive visual content while preserving essential cues for anomaly detection. It employs visual information suppression techniques, a dual-model evaluation architecture comprising an anomaly detection model and a privacy inference model, and a rank-based assessment strategy. Pareto front analysis is used to quantitatively characterize the trade-off between privacy preservation and task utility, enabling the identification of optimal operating points. Experiments on public datasets demonstrate that the framework substantially reduces exposure of personal data with only marginal performance degradation.

Abstract

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.

Problem

• video anomaly detection
• data minimization
• privacy preservation
• GDPR compliance
• personally identifiable information

Innovation

• data minimization
• privacy-preserving video anomaly detection
• Pareto optimality
• privacy-utility trade-off
• GDPR compliance