Summary of Large Language Models For Link Stealing Attacks Against Graph Neural Networks, by Faqian Guan et al.
Large Language Models for Link Stealing Attacks Against Graph Neural Networks
by Faqian Guan, Tianqing Zhu, Hui Sun, Wanlei Zhou, Philip S. Yu
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Graph Neural Network (GNN) attack method is proposed that leverages Large Language Models (LLMs) to perform link stealing attacks on GNNs. The approach effectively integrates textual features and exhibits strong generalizability, allowing it to handle diverse data dimensions across various datasets. Two distinct LLM prompts are designed to combine textual features and posterior probabilities of graph nodes, enabling fine-tuning of the LLM for the link stealing attack task. Experimental results demonstrate significant enhancements in performance for existing link stealing attack tasks in both white-box and black-box scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to hack Graph Neural Networks (GNNs) by using special language models called Large Language Models (LLMs). GNNs are good at handling graph data, but they can be vulnerable to attacks. The LLMs help the attackers by combining information from text and graphs to make better guesses about whether two nodes are linked or not. This new approach is tested on different datasets and shows that it can be used to hack GNNs in real-world scenarios. |
Keywords
* Artificial intelligence * Fine tuning * Gnn * Graph neural network
