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

Khondker Reza, Md Islam, Vladimir Estivill-Castro

Abstract

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.

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Paper Citation


in Harvard Style

Reza K., Islam M. and Estivill-Castro V. (2019). Privacy Preservation of Social Network Users Against Attribute Inference Attacks via Malicious Data Mining.In Proceedings of the 5th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-359-9, pages 412-420. DOI: 10.5220/0007390404120420


in Bibtex Style

@conference{icissp19,
author={Khondker Reza and Md Islam and Vladimir Estivill-Castro},
title={Privacy Preservation of Social Network Users Against Attribute Inference Attacks via Malicious Data Mining},
booktitle={Proceedings of the 5th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2019},
pages={412-420},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007390404120420},
isbn={978-989-758-359-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Privacy Preservation of Social Network Users Against Attribute Inference Attacks via Malicious Data Mining
SN - 978-989-758-359-9
AU - Reza K.
AU - Islam M.
AU - Estivill-Castro V.
PY - 2019
SP - 412
EP - 420
DO - 10.5220/0007390404120420