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

arXiv: https://arxiv.org/abs/2609.12448 · HTML: https://mlsanity.com/paper/2609.12448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 85 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Are self-explanations from Large Language Models faithful?](https://mlsanity.com/paper/2401.07927/) (2024)
- [The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization](https://mlsanity.com/paper/2202.00443/) (2022)
- [PANDORA Talks: Personality and Demographics on Reddit](https://mlsanity.com/paper/2004.04460/) (2020)
- [A Synthetic Dataset for Personal Attribute Inference](https://mlsanity.com/paper/2406.07217/) (2024)
- [IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization](https://mlsanity.com/paper/2407.02956/) (2024)
- [Automated Profile Inference with Language Model Agents](https://mlsanity.com/paper/2505.12402/) (2025)
- [Stop Tracking Me! Proactive Defense Against Attribute Inference Attack in LLMs](https://mlsanity.com/paper/2602.11528/) (2026)
- [Geocoding Without Geotags: A Text-based Approach for reddit](https://mlsanity.com/paper/1810.03067/) (2018)

## Abstract (arXiv)

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 high accuracy, which makes large-scale personal attribute inference a major privacy threat. Existing LLM-based profilers, however, offer limited insight into which specific posts, concepts, and relationships made an inference possible, which is key to targeted privacy mitigation, i.e., redacting or rewriting only the few posts that actually leak an attribute, rather than perturbing entire histories. We introduce GraphProfiler, an auditable LLM-based profiler that represents each user's post history as a source-linked personal knowledge graph where nodes and edges trace back to the originating post and resolves attribute predictions to cited graph records and source texts. GraphProfiler reaches 86.7% attack success rate on the eight-attribute SynthPAI benchmark, within two points of strong text-only baselines, and 84.6% on PANDORA, while citing supporting evidence for over 98% of predictions. Our controlled ablation experiments provide evidence that the cited posts contribute to attack success, as removing them reduces the attack success rate substantially more than removing an equal number of random posts.