A privacy-preserving model to control social interaction behaviors in social network sites

被引:10
|
作者
Kavianpour, Sanaz [1 ]
Tamimi, Ali [2 ]
Shanmugam, Bharanidharan [3 ]
机构
[1] Abertay Univ, Dundee, Scotland
[2] Washington State Univ, Pullman, WA 99164 USA
[3] Charles Darwin Univ, Darwin, NT, Australia
关键词
Anonymization; Classification; Privacy; Social interaction behaviors; Social network sites; K-ANONYMITY; ANONYMIZATION;
D O I
10.1016/j.jisa.2019.102402
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Social Network Sites (SNSs) served as an invaluable platform to transfer information across a large number of users. SNSs also disseminate users data to third-parties to provide more interesting services for users as well as gaining profits. Users grant access to third-parties to use their services, although they do not necessarily protect users' data privacy. Controlling social network data diffusion among users and third-parties is difficult due to the vast amount of data. Hence, undesirable users' data diffusion to unauthorized parties in SNSs may endanger users' privacy. This paper highlights the privacy breaches on SNSs and emphasizes the most significant privacy issues to users. The goals of this paper are to i) propose a privacy-preserving model for social interactions among users and third-parties; ii) enhance users' privacy by providing access to the data for appropriate third-parties. These advocate to not compromising the advantages of SNSs information sharing functionalities. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页数:8
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