Privacy-Preserving Graph Embedding based on Local Differential Privacy
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913456755572736 |
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| author | Li, Zening Li, Rong-Hua Liao, Meihao Jin, Fusheng Wang, Guoren |
| author_facet | Li, Zening Li, Rong-Hua Liao, Meihao Jin, Fusheng Wang, Guoren |
| contents | Graph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks, serious privacy concerns arise when the graph data contains personal or sensitive information. To address this issue, we investigate and develop graph embedding algorithms that satisfy local differential privacy (LDP). We introduce a novel privacy-preserving graph embedding framework, named PrivGE, to protect node data privacy. Specifically, we propose an LDP mechanism to obfuscate node data and utilize personalized PageRank as the proximity measure to learn node representations. Furthermore, we provide a theoretical analysis of the privacy guarantees and utility offered by the PrivGE framework. Extensive experiments on several real-world graph datasets demonstrate that PrivGE achieves an optimal balance between privacy and utility, and significantly outperforms existing methods in node classification and link prediction tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_11060 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Graph Embedding based on Local Differential Privacy Li, Zening Li, Rong-Hua Liao, Meihao Jin, Fusheng Wang, Guoren Cryptography and Security Machine Learning Social and Information Networks Graph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks, serious privacy concerns arise when the graph data contains personal or sensitive information. To address this issue, we investigate and develop graph embedding algorithms that satisfy local differential privacy (LDP). We introduce a novel privacy-preserving graph embedding framework, named PrivGE, to protect node data privacy. Specifically, we propose an LDP mechanism to obfuscate node data and utilize personalized PageRank as the proximity measure to learn node representations. Furthermore, we provide a theoretical analysis of the privacy guarantees and utility offered by the PrivGE framework. Extensive experiments on several real-world graph datasets demonstrate that PrivGE achieves an optimal balance between privacy and utility, and significantly outperforms existing methods in node classification and link prediction tasks. |
| title | Privacy-Preserving Graph Embedding based on Local Differential Privacy |
| topic | Cryptography and Security Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2310.11060 |