Local Differential Privacy Graph Data Modeling Method for Link Prediction
To solve the problem of node sensitive link privacy being exposed in the process of link prediction on industrial business graph data , according to the theory of local differential privacy , the shortcomings of the existing graph privacy protection technology are analyzed from the perspective of...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2023-10-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2257 |
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| Summary: | To solve the problem of node sensitive link privacy being exposed in the process of link prediction on industrial business graph data , according to the theory of local differential privacy , the shortcomings of the existing graph privacy protection technology are analyzed from the perspective of link prediction task performance. Based on the existing randomized response mechanism , it introduces the personalized sampling technology to reduce the noise addition on the user side. At the same time , combined with the subgraph partitioning strategy of two rounds of data collection , the subgraph cluster feature of the original graph is retained. Finally , a personalized sampling randomized response local differential privacy ( PSRR-LDP) graph data perturbing algorithm was implemented , and the PSRR-LDP algorithm is theoretically proved to satisfy the ε -edge Local differential privacy. The simulation experiments show that the PSRR-LDP algorithm has better link prediction performance while ensuring privacy. |
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| ISSN: | 1007-2683 |