{"ID":2887794,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00321","arxiv_id":"2508.00321","title":"Evaluating the Efficacy of Large Language Models for Generating Fine-Grained Visual Privacy Policies in Homes","abstract":"The proliferation of visual sensors in smart home environments, particularly through wearable devices like smart glasses, introduces profound privacy challenges. Existing privacy controls are often static and coarse-grained, failing to accommodate the dynamic and socially nuanced nature of home environments. This paper investigates the viability of using Large Language Models (LLMs) as the core of a dynamic and adaptive privacy policy engine. We propose a conceptual framework where visual data is classified using a multi-dimensional schema that considers data sensitivity, spatial context, and social presence. An LLM then reasons over this contextual information to enforce fine-grained privacy rules, such as selective object obfuscation, in real-time. Through a comparative evaluation of state-of-the-art Vision Language Models (including GPT-4o and the Qwen-VL series) in simulated home settings , our findings show the feasibility of this approach. The LLM-based engine achieved a top machine-evaluated appropriateness score of 3.99 out of 5, and the policies generated by the models received a top human-evaluated score of 4.00 out of 5.","short_abstract":"The proliferation of visual sensors in smart home environments, particularly through wearable devices like smart glasses, introduces profound privacy challenges. Existing privacy controls are often static and coarse-grained, failing to accommodate the dynamic and socially nuanced nature of home environments. This paper...","url_abs":"https://arxiv.org/abs/2508.00321","url_pdf":"https://arxiv.org/pdf/2508.00321v1","authors":"[\"Shuning Zhang\",\"Ying Ma\",\"Xin Yi\",\"Hewu Li\"]","published":"2025-08-01T05:11:29Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}