A Novel k-Anonymization Approach to Prevent Insider Attack in Collaborative Social Network Data Publishing

2019 ◽  
pp. 255-275
Author(s):  
Bintu Kadhiwala ◽  
Sankita J. Patel
Keyword(s):  
Social Network ◽  
Data Publishing ◽  
Network Data ◽  
Social Network Data ◽  
Insider Attack

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.

k-NMF Anonymization in Social Network Data Publishing

2018 ◽  
Vol 61 (4) ◽  
pp. 601-613 ◽  
Author(s):  
Kamalkumar R Macwan ◽  
Sankita J Patel
Keyword(s):  
Social Network ◽  
Data Publishing ◽  
Network Data ◽  
Social Network Data
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