Privacy preservation of social network users against attribute inference attacks via malicious data mining

Khondker Jahid Reza, M. Zahidul Islam, Vladimir Estivill-Castro

Research output: Book chapter/Published conference paperConference paper

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.
Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Information Systems Security and Privacy
Subtitle of host publicationICISSP 2019
EditorsPaolo Mori, Olivier Camp, Steven Furnell
PublisherScitepress
Pages412-420
Number of pages9
ISBN (Electronic)9789897583599
Publication statusPublished - 23 Feb 2019
Event5th International Conference on Information Systems Security and Privacy: ICISSP 2019 - Prague, Czech Republic
Duration: 23 Feb 201925 Feb 2019

Publication series

NameICISSP 2019 - Proceedings of the 5th International Conference on Information Systems Security and Privacy

Conference

Conference5th International Conference on Information Systems Security and Privacy
CountryCzech Republic
CityPrague
Period23/02/1925/02/19

Fingerprint

Data mining

Cite this

Reza, K. J., Islam, M. Z., & Estivill-Castro, V. (2019). Privacy preservation of social network users against attribute inference attacks via malicious data mining. In P. Mori, O. Camp, & S. Furnell (Eds.), Proceedings of the 5th International Conference on Information Systems Security and Privacy: ICISSP 2019 (pp. 412-420). (ICISSP 2019 - Proceedings of the 5th International Conference on Information Systems Security and Privacy). Scitepress.
Reza, Khondker Jahid ; Islam, M. Zahidul ; Estivill-Castro, Vladimir. / Privacy preservation of social network users against attribute inference attacks via malicious data mining. Proceedings of the 5th International Conference on Information Systems Security and Privacy: ICISSP 2019. editor / Paolo Mori ; Olivier Camp ; Steven Furnell. Scitepress, 2019. pp. 412-420 (ICISSP 2019 - Proceedings of the 5th International Conference on Information Systems Security and Privacy).
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title = "Privacy preservation of social network users against attribute inference attacks via malicious data mining",
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.",
keywords = "Attribute Inference, Data Mining, Privacy Protection Technique",
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Reza, KJ, Islam, MZ & Estivill-Castro, V 2019, Privacy preservation of social network users against attribute inference attacks via malicious data mining. in P Mori, O Camp & S Furnell (eds), Proceedings of the 5th International Conference on Information Systems Security and Privacy: ICISSP 2019. ICISSP 2019 - Proceedings of the 5th International Conference on Information Systems Security and Privacy, Scitepress, pp. 412-420, 5th International Conference on Information Systems Security and Privacy, Prague, Czech Republic, 23/02/19.

Privacy preservation of social network users against attribute inference attacks via malicious data mining. / Reza, Khondker Jahid; Islam, M. Zahidul; Estivill-Castro, Vladimir.

Proceedings of the 5th International Conference on Information Systems Security and Privacy: ICISSP 2019. ed. / Paolo Mori; Olivier Camp; Steven Furnell. Scitepress, 2019. p. 412-420 (ICISSP 2019 - Proceedings of the 5th International Conference on Information Systems Security and Privacy).

Research output: Book chapter/Published conference paperConference paper

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AB - 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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Reza KJ, Islam MZ, Estivill-Castro V. Privacy preservation of social network users against attribute inference attacks via malicious data mining. In Mori P, Camp O, Furnell S, editors, Proceedings of the 5th International Conference on Information Systems Security and Privacy: ICISSP 2019. Scitepress. 2019. p. 412-420. (ICISSP 2019 - Proceedings of the 5th International Conference on Information Systems Security and Privacy).