Differentially Private Densest Subgraph Detection
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arXiv
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866913376464011264 |
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| author | Nguyen, Dung Vullikanti, Anil |
| author_facet | Nguyen, Dung Vullikanti, Anil |
| contents | Densest subgraph detection is a fundamental graph mining problem, with a large number of applications. There has been a lot of work on efficient algorithms for finding the densest subgraph in massive networks. However, in many domains, the network is private, and returning a densest subgraph can reveal information about the network. Differential privacy is a powerful framework to handle such settings. We study the densest subgraph problem in the edge privacy model, in which the edges of the graph are private. We present the first sequential and parallel differentially private algorithms for this problem. We show that our algorithms have an additive approximation guarantee. We evaluate our algorithms on a large number of real-world networks, and observe a good privacy-accuracy tradeoff when the network has high density. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2105_13287 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Differentially Private Densest Subgraph Detection Nguyen, Dung Vullikanti, Anil Data Structures and Algorithms Artificial Intelligence Cryptography and Security Machine Learning Densest subgraph detection is a fundamental graph mining problem, with a large number of applications. There has been a lot of work on efficient algorithms for finding the densest subgraph in massive networks. However, in many domains, the network is private, and returning a densest subgraph can reveal information about the network. Differential privacy is a powerful framework to handle such settings. We study the densest subgraph problem in the edge privacy model, in which the edges of the graph are private. We present the first sequential and parallel differentially private algorithms for this problem. We show that our algorithms have an additive approximation guarantee. We evaluate our algorithms on a large number of real-world networks, and observe a good privacy-accuracy tradeoff when the network has high density. |
| title | Differentially Private Densest Subgraph Detection |
| topic | Data Structures and Algorithms Artificial Intelligence Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2105.13287 |