Securely Publishing Social Network Data
Emad Elabd1,
Hatem AbdulKader1, and Waleed Ead2
1Faculty of computers and
information, Menoufia University, Egypt
2Faculty
of Computers and Information, Beni-Suef University, Egypt
Abstract: Online Social Networks (OSNs) data are published to be
used for the purpose of analysis in scientific research. Yet, offering such
data in its crude structure raises serious privacy concerns. An adversary may
attack the privacy of certain victims easily by collecting local background
knowledge about individuals in a social network such as information about its
neighbors. The subgraph attack that is based on frequent pattern mining and
members’ background information may be used to breach the privacy in the published
social networks. Most of the current anonymization approaches do not guarantee
the privacy preserving of identities from attackers in case of using the
frequent pattern mining and background knowledge. In this paper, a secure
k-anonymity algorithm that protects published social networks data against
subgraph attacks using background information and frequent pattern mining is
proposed. The proposed approach has been implemented and tested on real
datasets. The experimental results show that the anonymized OSNs can preserve
the major characteristics of original OSNs as a tradeoff between privacy and
utility.
Keywords: Data publishing, privacy preserving, online
social networks, background knowledge, anonymization, frequent pattern mining.
Received May 7, 2016; accepted June 12, 2017
