Paper Info
Title
Anonymization in Online Social Networks Based on Enhanced Equi-Cardinal Clustering
Abstract
Recent trends show that the popularity of online social networks (OSNs) has been increasing rapidly. From daily communication sites to online communities, an average person’s daily life has become dependent on these online networks. Hence, it has become evident that protection should be provided to these networks from unwanted intruders. In this paper, we consider the data privacy on OSNs at the network level rather than the user level. This network-level privacy helps us to prevent information leakage to third-party users, such as advertisers. We propose a novel scheme that combines the privacy of all the elements of a social network: node, edge, and attribute privacy by clustering the users based on their attribute similarity. We use an enhanced equi-cardinal clustering (ECC) as a way to achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -anonymity. We further improve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -anonymity with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> -diversity. Our proposed enhanced ECC ensures that there are at least “ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> ” users in any given network as well as the attributes in each cluster has at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula> -distinct values. We further provide proofs on how the proposed ECC ensures <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -anonymity and the maximum information loss. We consider a weighted directed social network graph as an input to our method to consider the existing complexities in a social network. With the help of two real-world data sets, we evaluate this method in terms of privacy and efficiency.
Year
DOI
Venue
2019
10.1109/TCSS.2019.2928324
IEEE Transactions on Computational Social Systems
Keywords
Field
DocType
Privacy,Social networking (online),Computer science,Clustering algorithms,Knowledge engineering,Measurement,Distortion
Data mining,Social network,Information leakage,Computer science,Popularity,Theoretical computer science,Mathematical proof,Knowledge engineering,Anonymity,Information privacy,Cluster analysis
Journal
Volume
Issue
ISSN
6
4
2329-924X
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Madhuri Siddula103.72
Yingshu Li267153.71
Xiuzhen Cheng33238210.23
Zhi Tian4119580.41
Zhipeng Cai51928132.81