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A Privacy Measurement Framework for Multiple Online Social Networks against Social Identity Linkage

Authors :
Xuefeng Li
Yixian Yang
Yuling Chen
Xinxin Niu
Source :
Applied Sciences, Vol 8, Iss 10, p 1790 (2018)
Publication Year :
2018
Publisher :
MDPI AG, 2018.

Abstract

Recently, the number of people who are members of multiple online social networks simultaneously has increased. However, if these people share everything with others, they risk their privacy. Users may be unaware of the privacy risks involved with sharing their sensitive information on a network. Currently, there are many research efforts focused on social identity linkage (SIL) on multiple online social networks for commercial services, which exacerbates privacy issues. Many existing studies consider methods of encrypting or deleting sensitive information without considering if this is unreasonable for social networks. Meanwhile, these studies ignore privacy awareness, which is rudimentary and critical. To enhance privacy awareness, we discuss a user privacy exposure measure for users who are members of multiple online social networks. With this measure, users can be aware of the state of their privacy and their position on a privacy measurement scale. Additionally, we propose a straightforward method through our framework to reduce information loss and foster user privacy awareness by using spurious content for required fields.

Details

Language :
English
ISSN :
20763417
Volume :
8
Issue :
10
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.6771f65124a04a40abb95fcc40a6a96a
Document Type :
article
Full Text :
https://doi.org/10.3390/app8101790