On-Device Generative AI for GDPR-Compliant Visual Monitoring: Natural Language Alerts from Local Object Detection
This paper presents a privacy-by-design pipeline for visual monitoring that confines all inference to the edge device. Using YOLOv5n-seg on a Raspberry Pi 5 with Hailo-8L accelerator, raw pixel buffers are discarded immediately after inference. A stateful trigger engine sends minimal JSON event payloads to a locally hosted Phi-3 Mini LLM, which generates natural language alerts. No image data ever leaves the device, ensuring GDPR compliance.
[2605.30544] On-Device Generative AI for GDPR-Compliant Visual Monitoring: Natural Language Alerts from Local Object Detection
[Submitted on 28 May 2026]
Title:On-Device Generative AI for GDPR-Compliant Visual Monitoring: Natural Language Alerts from Local Object Detection
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Abstract:Visual monitoring systems that rely on cloud-based AI inference expose raw image data to external services, creating fundamental tensions with the data-minimisation principle of the General Data Protection Regulation (GDPR). This paper presents a proof-of-concept privacy-by-design pipeline that resolves this tension by confining all inference entirely to the edge device. A YOLOv5n-seg model compiled for a Hailo-8L AI accelerator delivers real-time object detection on a Raspberry Pi 5, from which raw pixel buffers are immediately discarded after inference. A stateful trigger engine forwards minimal JSON event payloads to a locally hosted instance of Phi-3 Mini (3.8B parameters, Q4_0 quantisation), which synthesises one-to-two sentence natural-language alerts for a human operator. No image data crosses the network boundary at any point; only the generated text alert is transmitted. We describe the full system architecture and implementation, report measured inference latency and resource utilisation on the target hardware, and present representative generated alerts. The results demonstrate that combining a dedicated neural-network accelerator with an on-device large language model on a single-board computer is not only feasible but produces practically deployable, human-readable monitoring output while aligning with GDPR Art. 5(1)(c) by design.
Comments: 6 pages, 4 figures, 3 tables, 1 listing
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
MSC classes: 68T45, 68T50
ACM classes: I.4.7; I.2.7; K.4.1
Cite as: arXiv:2605.30544 [cs.CV]
(or arXiv:2605.30544v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.30544
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Gudrun Schappacher-Tilp [view email] [v1] Thu, 28 May 2026 20:21:52 UTC (2,801 KB)
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