A Content-Based Approach for User Profile Modeling and Matching on Social Networks

2014 ◽  
pp. 232-243 ◽  
Author(s):  
Thanh Van Le ◽  
Trong Nghia Truong ◽  
Tran Vu Pham
Keyword(s):  
Social Networks ◽  
User Profile ◽  
Profile Modeling

Colour as an Indicator of Vulnerability of Users Within Social Networks

2016 ◽  
Vol 8 (1) ◽  
pp. 56-69
Author(s):  
Amanda Cox ◽  
Yeslam Al-Saggaf ◽  
Kate McLean
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Networking ◽  
User Profile ◽  
Risk Matrix ◽  
Individual User ◽  
Privacy Settings ◽  
Personally Identifiable Information ◽  
The Individual

Social networking users are presented with a plethora of profile and privacy settings; most of which are left defaulted. As a result, there is little understanding of the fields that make up the user profile, the privacy settings available to safeguard the user, and the ramifications of not changing the same. Concerns relating to the unprecedented quantities of Personally Identifiable Information being stored need to be addressed. By employing a risk matrix to a social media profile, a user could be alerted to the potential dangers of the information being contained within the profile. By adapting this tool, the risks to the individual user of a social media profile will be minimised.

scholarly journals Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 12031-12040 ◽  
Author(s):  
Jiangtao Ma ◽  
Yaqiong Qiao ◽  
Guangwu Hu ◽  
Yongzhong Huang ◽  
Meng Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Online Social Networks ◽  
User Profile ◽  
Social Network Structure

Privacy Information Leakage Prevention in Cognitive Social Mining Applications

2019 ◽  
pp. 188-212
Author(s):  
Suriya Murugan ◽  
Anandakumar H.
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Private Information ◽  
Online Social Networks ◽  
Information Leakage ◽  
User Profile ◽  
User Privacy ◽  
Sensitive Data ◽  
Data Set ◽  
Secure Information

Online social networks, such as Facebook are increasingly used by many users and these networks allow people to publish and share their data to their friends. The problem is user privacy information can be inferred via social relations. This chapter makes a study and performs research on managing those confidential information leakages which is a challenging issue in social networks. It is possible to use learning methods on user released data to predict private information. Since the main goal is to distribute social network data while preventing sensitive data disclosure, it can be achieved through sanitization techniques. Then the effectiveness of those techniques is explored, and the methods of collective inference are used to discover sensitive attributes of the user profile data set. Hence, sanitization methods can be used efficiently to decrease the accuracy of both local and relational classifiers and allow secure information sharing by maintaining user privacy.

scholarly journals Factor Graph Model Based User Profile Matching Across Social Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 152429-152442
Author(s):  
Lidong Wang ◽  
Keyong Hu ◽  
Yun Zhang ◽  
Shihua Cao
Keyword(s):  
Social Networks ◽  
Graph Model ◽  
User Profile ◽  
Factor Graph ◽  
Model Based ◽  
Profile Matching ◽  
Factor Graph Model

scholarly journals Adaptive method of similarity detection of user profiles on online social networks

2019 ◽  
Vol 68 (2) ◽  
pp. 43-57
Author(s):  
Michał Zabielski ◽  
Zbigniew Tarapata ◽  
Rafał Kasprzyk
Keyword(s):  
Social Networks ◽  
Network Theory ◽  
Online Social Networks ◽  
User Profile ◽  
Adaptive Method ◽  
User Profiles ◽  
Simulation Environment ◽  
Similarity Detection ◽  
The Web

The paper presents a method, based on graph and network theory, which allows to detect cloned user profiles on Online Social Networks. Moreover, an idea of similarity containers, which gives an opportunity to incorporate importance and context of data into a model, was introduced. The presented solutions were adapted to the idea of simulation environment, which will allow to detect a profile cloning process before that activity will be completely performed by an attacker. Keywords: Online Social Networks, user profile cloning, violation of privacy on the web.

Privacy Information Leakage Prevention in Cognitive Social Mining Applications

2021 ◽  
pp. 2110-2134
Author(s):  
Suriya Murugan ◽  
Anandakumar H.
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Private Information ◽  
Online Social Networks ◽  
Information Leakage ◽  
User Profile ◽  
User Privacy ◽  
Sensitive Data ◽  
Data Set ◽  
Secure Information

Online social networks, such as Facebook are increasingly used by many users and these networks allow people to publish and share their data to their friends. The problem is user privacy information can be inferred via social relations. This chapter makes a study and performs research on managing those confidential information leakages which is a challenging issue in social networks. It is possible to use learning methods on user released data to predict private information. Since the main goal is to distribute social network data while preventing sensitive data disclosure, it can be achieved through sanitization techniques. Then the effectiveness of those techniques is explored, and the methods of collective inference are used to discover sensitive attributes of the user profile data set. Hence, sanitization methods can be used efficiently to decrease the accuracy of both local and relational classifiers and allow secure information sharing by maintaining user privacy.

Opinions matter: a general approach to user profile modeling for contextual suggestion

2015 ◽  
Vol 18 (6) ◽  
pp. 586-610 ◽  
Author(s):  
Peilin Yang ◽  
Hongning Wang ◽  
Hui Fang ◽  
Deng Cai
Keyword(s):  
User Profile ◽  
Profile Modeling
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