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

arXiv: https://arxiv.org/abs/2512.09463 · HTML: https://mlsanity.com/paper/2512.09463/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 8 of 8)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Deblurring by Realistic Blurring](https://mlsanity.com/paper/2004.01860/) (2020)
- [DeepPrivacy2: Towards Realistic Full-Body Anonymization](https://mlsanity.com/paper/2211.09454/) (2022)
- [Adversarial Scene Editing: Automatic Object Removal from Weak Supervision](https://mlsanity.com/paper/1806.01911/) (2018)
- [Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery](https://mlsanity.com/paper/1903.11532/) (2019)
- [Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges](https://mlsanity.com/paper/2101.10569/) (2021)
- [A False Sense of Privacy: Towards a Reliable Evaluation Methodology for the Anonymization of Biometric Data](https://mlsanity.com/paper/2304.01635/) (2023)
- [Enabling Privacy-Aware AI-Based Ergonomic Analysis](https://mlsanity.com/paper/2505.07306/) (2025)

## Abstract (arXiv)

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.