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New GraphProfiler tool traces LLM inferences to specific user posts

By PulseAugur Editorial · [1 sources] ·

Researchers have developed GraphProfiler, a new auditable LLM-based system designed to infer sensitive user attributes from online content. Unlike previous methods, GraphProfiler constructs a source-linked personal knowledge graph, allowing it to trace inferences back to specific posts and cited evidence. This approach aims to enhance privacy mitigation by identifying the exact content contributing to attribute leakage. The system achieved an 86.7% attack success rate on the SynthPAI benchmark and 84.6% on PANDORA, while providing supporting evidence for over 98% of its predictions. AI

IMPACT Enables more targeted privacy mitigation by identifying specific content that leaks sensitive user information.

RANK_REASON The item is a research paper detailing a new method for sensitive attribute inference using LLMs and knowledge graphs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New GraphProfiler tool traces LLM inferences to specific user posts

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The item is a research paper detailing a new method for sensitive attribute inference using LLMs and knowledge graphs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ahmed Sohair Khan, Estrid He, Chenglong Ma, Monica Wachowicz, Elham Naghizade ·

    GraphProfiler: Source-Linked Sensitive Attribute Inference via Personal Knowledge Graphs

    arXiv:2609.12448v1 Announce Type: new Abstract: Sensitive attributes such as age, income, and occupation can be inferred from user-generated content by aggregating indirect cues across many ordinary posts. LLM-based profilers can perform this aggregation automatically and with hi…

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