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

2026-09-11

AI Summary

为解决大规模个人属性推断隐私威胁，GraphProfiler通过构建源链接个人知识图谱，追踪具体帖子、概念和关系对属性推断的影响，实现高精度敏感属性推断的同时提供审计线索。

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 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.

Problem

• Sensitive Attributes
• Privacy Threat
• LLM-based Profilers
• Personal Knowledge Graphs
• Attribute Inference

Innovation

• Source-Linked Personal Knowledge Graph
• Sensitive Attribute Inference
• Auditable Profiler
• Targeted Privacy Mitigation