Preserving Privacy of Social Networks Data Against Mutual Friends and Degree Attacks
Amardeep Singh, Divya Bansal, Sanjeev Sofat
Journal of Information Organization, Vol. 8, No. 3, 2018.
DOI: https://doi.org/10.6025/jio/2018/8/3/83-97
Link: http://www.dline.info/jio/fulltext/v8n3/jiov8n3_1.pdf
Abstract:
Online social networks have become a predominant service on the web collecting huge amount of usersâ€™ information.
It is drastically revolutionizing the way people interact. Publishing data of social network users for researchers, academicians,
advertising organizations etc. has raised many serious privacy implications. Lots of techniques have been proposed
for preserving privacy of individuals handling different types of attack scenarios used by adversaries. In this paper, we address
a new attack i.e. mutual friends attack model, in which an adversary can identify the victim nodes by using knowledge about
the number of their mutual friends. An algorithm â€˜Optimized K-anonymizationâ€™ has been devised that can deal with two types
of attacks i.e. degree attacks and number of mutual friends attacks. The experimental results illustrate that our proposed
algorithm can preserve the identification of individuals and subsequently maintain the utility of data.
Classification: CCS â†’ Security and Privacy â†’ Human and Societal Aspects of Security and Privacy â†’ Privacy Protections