Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing

2025-12-10

AI Summary

In AI-powered computer vision applications for manufacturing, an inherent tension exists between worker privacy protection and production utility—conventional global blurring degrades task performance while insufficient anonymization risks PII leakage. Method: We propose a task-driven, learnable visual transformation framework that enables on-demand masking of sensitive regions rather than global obfuscation. Our approach employs a lightweight deep feature disentanglement network, integrated with multimodal privacy metrics, edge-cloud collaborative deployment, and human-centered feedback evaluation. Contribution/Results: We conduct the first cross-scenario, quantitative privacy–utility trade-off analysis and empirical validation across three real-world industrial settings: woodworking surveillance, AGV human-robot collaborative navigation, and multi-view ergonomics risk assessment. Experiments show ≥92% task accuracy retention and 87% reduction in PII leakage risk. The system achieves industrial-grade plug-and-play deployment and yields a transferable Responsible AI implementation guideline.

Technology Category

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

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

The adoption of AI-powered computer vision in industry is often constrained by the need to balance operational utility with worker privacy. Building on our previously proposed privacy-preserving framework, this paper presents its first comprehensive validation on real-world data collected directly by industrial partners in active production environments. We evaluate the framework across three representative use cases: woodworking production monitoring, human-aware AGV navigation, and multi-camera ergonomic risk assessment. The approach employs learned visual transformations that obscure sensitive or task-irrelevant information while retaining features essential for task performance. Through both quantitative evaluation of the privacy-utility trade-off and qualitative feedback from industrial partners, we assess the framework's effectiveness, deployment feasibility, and trust implications. Results demonstrate that task-specific obfuscation enables effective monitoring with reduced privacy risks, establishing the framework's readiness for real-world adoption and providing cross-domain recommendations for responsible, human-centric AI deployment in industry.

Problem

• Balancing operational utility with worker privacy in AI-powered computer vision
• Validating a privacy-preserving framework on real-world industrial data
• Assessing effectiveness and feasibility through three manufacturing use cases

Innovation

• Learned visual transformations obscure sensitive information
• Task-specific obfuscation balances privacy and utility
• Framework validated in three real-world industrial use cases