Privacy preservation of social network users against attribute inference attacks via malicious data mining
File version
Version of Record (VoR)
Author(s)
Reza, KJ
Islam, MZ
Estivill-Castro, V
Griffith University Author(s)
Year published
2019
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Conference Title
ICISSP 2019 - Proceedings of the 5th International Conference on Information Systems Security and Privacy
Volume
1
Copyright Statement
© 2019 ScitePress. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
Subject
Distributed computing and systems software