Privacy Preservation of Social Network Users Against Attribute Inference Attacks via Malicious Data Mining

被引：5

作者：

Reza, Khondker Jahid ^{[1
]}

Islam, Md Zahidul ^{[1
]}

Estivill-Castro, Vladimir ^{[2
]}

机构：

[1] Charles Sturt Univ, Sch Comp & Math, Panorama Ave, Bathurst, NSW 2795, Australia

[2] Univ Pompeu Fabra, Dept Tecnol Informacio & Comunicac, Roc Boronat 138, Barcelona 08018, Spain

来源：

PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY (ICISSP) | 2019年

关键词：

Attribute Inference; Data Mining; Privacy Protection Technique;

D O I：

10.5220/0007390404120420

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Online social networks (OSNs) are currently a popular platform for social interactions among people. Usually, OSN users upload various contents including personal information on their profiles. The ability to infer users' hidden information or information that has not been even uploaded (i.e. private/sensitive information) by an unauthorised agent is commonly known as attribute inference problem. In this paper, we propose 3LP+, a privacy-preserving technique, to protect users' sensitive information leakage. We apply 3LP+ on a synthetically generated OSN data set and demonstrate the superiority of 3LP+ over an existing privacy-preserving technique.

引用

页码：412 / 420

页数：9

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