Attribute Inference Attacks in Online Social Networks

2018 ◽  
Vol 21 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Neil Zhenqiang Gong ◽  
Bin Liu
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Inference Attacks

An Overview on Protecting User Private-Attribute Information on Social Networks

2021 ◽  
pp. 102-117
Author(s):  
Walaa Alnasser ◽  
Ghazaleh Beigi ◽  
Huan Liu
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Differential Privacy ◽  
Future Research ◽  
Online Publishing ◽  
Social Media Data ◽  
User Data ◽  
Inference Attacks ◽  
Media Data

Online social networks enable users to participate in different activities, such as connecting with each other and sharing different contents online. These activities lead to the generation of vast amounts of user data online. Publishing user-generated data causes the problem of user privacy as this data includes information about users' private and sensitive attributes. This privacy issue mandates social media data publishers to protect users' privacy by anonymizing user-generated social media data. Existing private-attribute inference attacks can be classified into two classes: friend-based private-attribute attacks and behavior-based private-attribute attacks. Consequently, various privacy protection models are proposed to protect users against private-attribute inference attacks such as k-anonymity and differential privacy. This chapter will overview and compare recent state-of-the-art researches in terms of private-attribute inference attacks and corresponding anonymization techniques. In addition, open problems and future research directions will be discussed.

Analysis of User Interactions in Online Social Networks

2011 ◽  
Author(s):  
Seokchan Yun ◽  
Heungseok Do ◽  
Jinuk Jung ◽  
Song Mina ◽  
Namgoong Hyun ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
User Interactions
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