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@article{MarijaJSA2025,

title = {Privacy-by-design AIoT Vision for Intelligent Urban Environments},

author = {Marija Ivanovska and Jakob Kreft and Vitomir Štruc and Janez Perš},

url = {https://www.sciencedirect.com/science/article/pii/S1383762125002589?via%3Dihub},

year  = {2025},

date = {2025-10-14},

journal = {Journal of Systems Architecture},

abstract = {The recent advancements in AI (Artificial Intelligence) have been instrumental in fostering the development of AIoT (Artificial Intelligence of Things)-enabled urban environments. Machine vision and image analysis, in particular, have become integral to a wide array of AI applications within the field of urban planning and monitoring. Yet, the rapid adoption of AI algorithms in public areas has significantly heightened privacy concerns. In this paper, we introduce a privacy-by-design approach tailored for intelligent urban systems, presenting a holistic approach to the development and deployment of AI-driven systems for privacy-preserving image acquisition and analysis. Specifically, we design an embedded vision system that acquires privacy-protected data, safeguarding sensitive information against unauthorized access and potential misuse. Furthermore, we propose a strategy for developing AI vision methods using data that has been anonymized, ensuring that privacy is maintained throughout the AI application building process. Through experiments on a real-world AIoT-enabled urban environment use case - traffic flow monitoring at a city intersection - we demonstrate that our approach upholds strong privacy guarantees while maintaining the operational performance of modern AI vision systems.},

keywords = {ai, AIoT, computer vision, intelligent environment, privacy},

pubstate = {published},

tppubtype = {article}

}