GraphProfiler: Source-Linked Sensitive Attribute Inference via Personal Knowledge Graphs
GraphProfiler links LLM attribute inferences to source posts via personal knowledge graphs, enabling targeted redaction of privacy-leaking content.
GraphProfiler represents a user's post history as a source-linked personal knowledge graph where nodes and edges trace back to originating posts, making LLM-based attribute inference auditable. It reaches 86.7% attack success rate on the eight-attribute SynthPAI benchmark and 84.6% on PANDORA, within two points of strong text-only baselines, while citing supporting evidence for over 98% of predictions. Ablation experiments show removing cited posts reduces attack success substantially more than removing random posts, supporting targeted privacy mitigation.
- Auditable LLM profiler builds source-linked personal knowledge graphs from post histories
- 86.7% attack success on SynthPAI; 84.6% on PANDORA, near text-only baselines
- Over 98% of attribute predictions cite supporting graph records and source texts
- Removing cited posts cuts attack success far more than removing random posts
From the original article
1 min read at arxiv.orgSensitive 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.
By Ahmed Sohair Khan · arxiv.org
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