Evaluation of Human Visual Privacy Protection: A Three-Dimensional Framework and Benchmark Dataset

2025-07-18

AI Summary

Existing evaluation methodologies for visual privacy protection lack multidimensional, human-perception-aligned quantitative metrics, hindering balanced assessment of privacy preservation, visual utility, and implementation cost. Method: This paper introduces the first three-dimensional evaluation framework—Privacy-Utility-Cost (PUC)—specifically designed for visual privacy protection, accompanied by HR-VISPR, a human-centered, publicly available benchmark dataset featuring fine-grained annotations of both biometric and non-biometric attributes. Crucially, the framework explicitly incorporates human visual perception modeling into its evaluation pipeline and employs interpretable metric designs. Contribution/Results: We conduct systematic, quantitative evaluations of 11 state-of-the-art methods. Experimental results reveal fundamental trade-offs between privacy protection and visual utility across approaches, empirically validating the framework’s interpretability, systematicity, and generalizability. This work establishes a new paradigm for objective, comparative assessment and optimization of visual privacy-preserving technologies.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Privacy
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Recent advances in AI-powered surveillance have intensified concerns over the collection and processing of sensitive personal data. In response, research has increasingly focused on privacy-by-design solutions, raising the need for objective techniques to evaluate privacy protection. This paper presents a comprehensive framework for evaluating visual privacy-protection methods across three dimensions: privacy, utility, and practicality. In addition, it introduces HR-VISPR, a publicly available human-centric dataset with biometric, soft-biometric, and non-biometric labels to train an interpretable privacy metric. We evaluate 11 privacy protection methods, ranging from conventional techniques to advanced deep-learning methods, through the proposed framework. The framework differentiates privacy levels in alignment with human visual perception, while highlighting trade-offs between privacy, utility, and practicality. This study, along with the HR-VISPR dataset, serves as an insightful tool and offers a structured evaluation framework applicable across diverse contexts.

Problem

• Develops a 3D framework to evaluate visual privacy protection methods
• Introduces HR-VISPR dataset for training interpretable privacy metrics
• Assesses trade-offs between privacy, utility, and practicality in AI surveillance

Innovation

• Three-dimensional framework for privacy evaluation
• HR-VISPR dataset with biometric labels
• Evaluates 11 privacy protection methods