Privacy preserving based on differential privacy for weighted social networks
Focusing on the weak protection problems in privacy preservation of weighted social networks publication,a privacy preserving method based on differential privacy was put forward for strong protection of edges and edge weights.The WSQuery query model was proposed meeting with differential privacy on...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2015-09-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2015165/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841539682563784704 |
|---|---|
| author | Li-hui LAN Shi-guang JU |
| author_facet | Li-hui LAN Shi-guang JU |
| author_sort | Li-hui LAN |
| collection | DOAJ |
| description | Focusing on the weak protection problems in privacy preservation of weighted social networks publication,a privacy preserving method based on differential privacy was put forward for strong protection of edges and edge weights.The WSQuery query model was proposed meeting with differential privacy on weighted social networks,could capture the structure of weighted social networks and returned the triple sequences as the query result set.The WSPA algorithm was designed according to the WSQuery model,could map the query result set into a real number vector and injected Laplace noise into the vector to realize privacy protection.The LWSPA algorithm was put forward because of the high error of the WSPA algorithm,partitioned the triples sequence of the query results into multiple subsequences,constructed the algorithms for each subsequence according with differential privacy and reduced the error and improved the data util-ity.The experimental results demonstrate that the proposed method can provide strong protection for privacy information,simultaneously the utility of the released weighted social networks is still acceptable. |
| format | Article |
| id | doaj-art-bdf8cdacab0f4e3ba398e60bb0301269 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2015-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-bdf8cdacab0f4e3ba398e60bb03012692025-01-14T06:53:36ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2015-09-013614515959695660Privacy preserving based on differential privacy for weighted social networksLi-hui LANShi-guang JUFocusing on the weak protection problems in privacy preservation of weighted social networks publication,a privacy preserving method based on differential privacy was put forward for strong protection of edges and edge weights.The WSQuery query model was proposed meeting with differential privacy on weighted social networks,could capture the structure of weighted social networks and returned the triple sequences as the query result set.The WSPA algorithm was designed according to the WSQuery model,could map the query result set into a real number vector and injected Laplace noise into the vector to realize privacy protection.The LWSPA algorithm was put forward because of the high error of the WSPA algorithm,partitioned the triples sequence of the query results into multiple subsequences,constructed the algorithms for each subsequence according with differential privacy and reduced the error and improved the data util-ity.The experimental results demonstrate that the proposed method can provide strong protection for privacy information,simultaneously the utility of the released weighted social networks is still acceptable.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2015165/weighted social networkprivacy preservingdifferential privacyquery modelLaplace distribution |
| spellingShingle | Li-hui LAN Shi-guang JU Privacy preserving based on differential privacy for weighted social networks Tongxin xuebao weighted social network privacy preserving differential privacy query model Laplace distribution |
| title | Privacy preserving based on differential privacy for weighted social networks |
| title_full | Privacy preserving based on differential privacy for weighted social networks |
| title_fullStr | Privacy preserving based on differential privacy for weighted social networks |
| title_full_unstemmed | Privacy preserving based on differential privacy for weighted social networks |
| title_short | Privacy preserving based on differential privacy for weighted social networks |
| title_sort | privacy preserving based on differential privacy for weighted social networks |
| topic | weighted social network privacy preserving differential privacy query model Laplace distribution |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2015165/ |
| work_keys_str_mv | AT lihuilan privacypreservingbasedondifferentialprivacyforweightedsocialnetworks AT shiguangju privacypreservingbasedondifferentialprivacyforweightedsocialnetworks |