PromptGraph: Graph-Guided Prompt Sanitization for Balancing Privacy and Utility in LLM Inference
PromptGraph applies graph analysis to detect and sanitise contextual privacy leakage in LLM prompts — going beyond simple PII masking to address inference-based re-identification when using external AI services.
Summary written by editorial AI · Source link below
arXiv:2607.10709v1 Announce Type: new Abstract: Large Language Model (LLM) services introduce a fundamental privacy challenge. Sensitive information may be inferred not only from explicit identifiers, such as names or phone numbers, but also from contextual associations among otherwise innocuous spans. Existing sanitizers typically assign privacy or utility signals to individual spans without explicitly modeling pairwise relationships among them. In this paper, we propose PromptGraph, a graph-g
Editorial Analysis
Enterprises sending prompts to external LLM APIs risk leaking personal data through contextual inference even after basic PII removal; graph-based sanitisation addresses this gap.
Pilot graph-based prompt sanitisation for LLM API calls that may contain contextually sensitive employee or customer data.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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