Date of Award
11-23-2020
Thesis Type
Masters
Document Type
Dissertation
Divisions
Faculty of Science
Department
Institute of Mathematical Science
Institution
University of Malaya
Abstract
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
Additional Information
Dissertation (M.A.) – Faculty of Science, Universiti Malaya, 2020.
Recommended Citation
Kah Meng, Chong, "Secure unlinkability schemes for privacy preserving data publishing in weighted social networks" (2020). Student Works (2020-2029). 516.
https://knova.um.edu.my/student_works_2020s/516
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Cybersecurity Commons, Databases and Information Systems Commons, Data Science Commons, Information Security Commons
Initial
maa