GRAND: Graph Release with Assured Node Differential Privacy
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909980629663744 |
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| author | Liu, Suqing Bi, Xuan Li, Tianxi |
| author_facet | Liu, Suqing Bi, Xuan Li, Tianxi |
| contents | Differential privacy is a well-established framework for safeguarding sensitive information in data. While extensively applied across various domains, its application to network data -- particularly at the node level -- remains underexplored. Existing methods for node-level privacy either focus exclusively on query-based approaches, which restrict output to pre-specified network statistics, or fail to preserve key structural properties of the network. In this work, we propose GRAND (Graph Release with Assured Node Differential privacy), which is, to the best of our knowledge, the first network release mechanism that releases networks while ensuring node-level differential privacy and preserving structural properties. Under a broad class of latent space models, we show that the released network asymptotically follows the same distribution as the original network. The effectiveness of the approach is evaluated through extensive experiments on both synthetic and real-world datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00402 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GRAND: Graph Release with Assured Node Differential Privacy Liu, Suqing Bi, Xuan Li, Tianxi Machine Learning Statistics Theory Methodology Differential privacy is a well-established framework for safeguarding sensitive information in data. While extensively applied across various domains, its application to network data -- particularly at the node level -- remains underexplored. Existing methods for node-level privacy either focus exclusively on query-based approaches, which restrict output to pre-specified network statistics, or fail to preserve key structural properties of the network. In this work, we propose GRAND (Graph Release with Assured Node Differential privacy), which is, to the best of our knowledge, the first network release mechanism that releases networks while ensuring node-level differential privacy and preserving structural properties. Under a broad class of latent space models, we show that the released network asymptotically follows the same distribution as the original network. The effectiveness of the approach is evaluated through extensive experiments on both synthetic and real-world datasets. |
| title | GRAND: Graph Release with Assured Node Differential Privacy |
| topic | Machine Learning Statistics Theory Methodology |
| url | https://arxiv.org/abs/2507.00402 |