Secure unlinkability schemes for privacy preserving data publishing in weighted social networks / Chong Kah Meng
Preserving privacy of users has been one of the important research issues in social networks. Social networks contain sensitive personal information that are often released for business and research purposes. The privacy of a user can be breached if the data are not released in an anonymized form. I...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
2020
|
| Subjects: | |
| Online Access: | http://studentsrepo.um.edu.my/13223/1/Chong_Kah_Meng.pdf http://studentsrepo.um.edu.my/13223/2/Chong_Kah_Meng.pdf http://studentsrepo.um.edu.my/13223/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Preserving privacy of users has been one of the important research issues in social networks. Social networks contain sensitive personal information that are often released for business and research purposes. The privacy of a user can be breached if the data are not released in an anonymized form. In this thesis, we address edge weight disclosure, link disclosure and identity disclosure problems in publishing weighted network data. To counter these privacy risks while preserving high utility of the published data, we define two key privacy properties, namely edge weight unlinkability and node unlinkability. We design two novel anonymization schemes namely MinSwap and |
|---|