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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2023-10-01
|
| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2257 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849719368149106688 |
|---|---|
| author | HANQilong WUXiaoming |
| author_facet | HANQilong WUXiaoming |
| author_sort | HANQilong |
| collection | DOAJ |
| description |
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. |
| format | Article |
| id | doaj-art-e502a63aceee43a9be228293d46e2f1a |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2023-10-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-e502a63aceee43a9be228293d46e2f1a2025-08-20T03:12:09ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832023-10-012805516010.15938/j.jhust.2023.05.007Local Differential Privacy Graph Data Modeling Method for Link PredictionHANQilong0WUXiaoming1CollegeofComputerScienceandTechnology,HarbinEngineeringUniversity,Harbin150001,ChinaLawSchool,HarbinUniversityofCommerce,Harbin150028,China 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.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2257privacy-preservingtechniqueslinkpredictionlocaldifferentialprivacypersonalizedsamplinggraphdatacollection |
| spellingShingle | HANQilong WUXiaoming Local Differential Privacy Graph Data Modeling Method for Link Prediction Journal of Harbin University of Science and Technology privacy-preservingtechniques linkprediction localdifferentialprivacy personalizedsampling graphdatacollection |
| title | Local Differential Privacy Graph Data Modeling Method for Link Prediction |
| title_full | Local Differential Privacy Graph Data Modeling Method for Link Prediction |
| title_fullStr | Local Differential Privacy Graph Data Modeling Method for Link Prediction |
| title_full_unstemmed | Local Differential Privacy Graph Data Modeling Method for Link Prediction |
| title_short | Local Differential Privacy Graph Data Modeling Method for Link Prediction |
| title_sort | local differential privacy graph data modeling method for link prediction |
| topic | privacy-preservingtechniques linkprediction localdifferentialprivacy personalizedsampling graphdatacollection |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2257 |
| work_keys_str_mv | AT hanqilong localdifferentialprivacygraphdatamodelingmethodforlinkprediction AT wuxiaoming localdifferentialprivacygraphdatamodelingmethodforlinkprediction |