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Privacy threat analysis of mobile social network data publishing

Version 2 2024-06-03, 11:57
Version 1 2019-04-01, 15:57
conference contribution
posted on 2024-06-03, 11:57 authored by Jemal AbawajyJemal Abawajy, MIH Ninggal, ZA Aghbari, AB Darem, A Alhashmi
With mobile phones becoming integral part of modern life, the popularity of mobile social networking has tremendously increased over the past few years, bringing with it many benefits but also new trepidations. In particular, privacy issues in mobile social networking has recently become a significant concern. In this paper we present our study on the privacy vulnerability of the mobile social network data publication with emphases on a re-identification and disclosure attacks. We present a new technique for uniquely identifying a targeted individual in the anonymized social network graph and empirically demonstrate the capability of the proposed approach using a very large social network datasets. The results show that the proposed approach can uniquely re-identify a target on anonymized social network data with high success rate.

History

Volume

239

Pagination

60-68

Location

Niagara Falls, Ont.

Start date

2017-10-22

End date

2017-10-25

ISSN

1867-8211

ISBN-13

9783319788159

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

Editor/Contributor(s)

Lin X, Ghorbani A, Ren K, Zhu S, Zhang A

Title of proceedings

SecureComm 2017: Proceedings of the 13th International Conference on Security and Privacy in Communication Networks

Event

European Alliance for Innovation. Conference (13th : 2017 : Niagara Falls, Ont.)

Publisher

Springer

Place of publication

Cham, Switzerland

Series

European Alliance for Innovation Conference

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