SBAC uses sketching and multimodal LLMs to help users refine underspecified access control preferences into complete, validated policies through iterative human-AI collaboration.
Say what you mean: Natural language access control with large language models for internet of things
3 Pith papers cite this work. Polarity classification is still indexing.
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NLAC architecture translates natural language requests to access policies via LLMs, with embedding-based subgraph selection enabling up to 98.7% accuracy on large networks per NLACBench evaluations.
LLMs reflect users' privacy preferences in access control decisions with up to 86% agreement and can promote safer behavior, but personalization trades off higher individual match for potentially less secure results when users over-permission.
citing papers explorer
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Sketch-based Access Control: A Multimodal Interface for Translating User Preferences into Intent-Aligned Policies
SBAC uses sketching and multimodal LLMs to help users refine underspecified access control preferences into complete, validated policies through iterative human-AI collaboration.
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Natural Language Access Control (NLAC): From Help Desk Requests to Structured Policies
NLAC architecture translates natural language requests to access policies via LLMs, with embedding-based subgraph selection enabling up to 98.7% accuracy on large networks per NLACBench evaluations.
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Can LLMs Make (Personalized) Access Control Decisions?
LLMs reflect users' privacy preferences in access control decisions with up to 86% agreement and can promote safer behavior, but personalization trades off higher individual match for potentially less secure results when users over-permission.