A Study on Privacy-Preserving Approaches in Online Social Network for Data Publishing

2018 ◽  
pp. 99-115
Author(s):  
S. Sathiya Devi ◽  
R. Indhumathi
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Privacy Preserving ◽  
Data Publishing

Privacy Preserving Approaches for Online Social Network Data Publishing

2021 ◽  
pp. 119-132
Author(s):  
Kamalkumar Macwan ◽  
Sankita Patel
Keyword(s):  
Social Network ◽  
Privacy Preservation ◽  
Differential Privacy ◽  
Online Social Network ◽  
Data Publishing ◽  
Network Data ◽  
User Privacy ◽  
Social Network Data ◽  
Huge Data ◽  
Popularity Prediction

Recently, the social network platforms have gained the attention of people worldwide. People post, share, and update their views freely on such platforms. The huge data contained on social networks are utilized for various purposes like research, market analysis, product popularity, prediction, etc. Although it provides so much useful information, it raises the issue regarding user privacy. This chapter discusses the various privacy preservation methods applied to the original social network dataset to preserve privacy against attacks. The two areas for privacy preservation approaches addressed in this chapter are anonymization in social network data publication and differential privacy in node degree publishing.

scholarly journals Safebook: A privacy-preserving online social network leveraging on real-life trust

2009 ◽  
Vol 47 (12) ◽  
pp. 94-101 ◽  
Author(s):  
Leucio Cutillo ◽  
Refik Molva ◽  
Thorsten Strufe
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Real Life ◽  
Privacy Preserving

scholarly journals A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 221330-221351
Author(s):  
Munmun Bhattacharya ◽  
Sandip Roy ◽  
Kamlesh Mistry ◽  
Hubert P. H. Shum ◽  
Samiran Chattopadhyay
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Privacy Preserving ◽  
Network Applications ◽  
Location Sharing ◽  
Sharing Scheme

scholarly journals A Privacy-Enhanced Friending Approach for Users on Multiple Online Social Networks

Computers ◽  
2018 ◽  
Vol 7 (3) ◽  
pp. 42 ◽  
Author(s):  
Erfan Aghasian ◽  
Saurabh Garg ◽  
James Montgomery
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Online Social Network ◽  
Privacy Preserving ◽  
User Participation ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
Sensitivity Score

Online social network users share their information in different social sites to establish connections with individuals with whom they want to be a friend. While users share all their information to connect to other individuals, they need to hide the information that can bring about privacy risks for them. As user participation in social networking sites rises, the possibility of sharing information with unknown users increases, and the probability of privacy breaches for the user mounts. This work addresses the challenges of sharing information in a safe manner with unknown individuals. Currently, there are a number of available methods for preserving privacy in order to friending (the act of adding someone as a friend), but they only consider a single source of data and are more focused on users’ security rather than privacy. Consequently, a privacy-preserving friending mechanism should be considered for information shared in multiple online social network sites. In this paper, we propose a new privacy-preserving friending method that helps users decide what to share with other individuals with the reduced risk of being exploited or re-identified. In this regard, the first step is to calculate the sensitivity score for individuals using Bernstein’s polynomial theorem to understand what sort of information can influence a user’s privacy. Next, a new model is applied to anonymise the data of users who participate in multiple social networks. Anonymisation helps to understand to what extent a piece of information can be shared, which allows information sharing with reduced risks in privacy. Evaluation indicates that measuring the sensitivity of information besides anonymisation provides a more accurate outcome for the purpose of friending, in a computationally efficient manner.

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