  To date publish of a giant social network jointly from different parties is
an easier collaborative approach. Agencies and researchers who collect such
social network data often have a compelling interest in allowing others to
analyze the data. In many cases the data describes relationships that are
private and sharing the data in full can result in unacceptable disclosures.
Thus, preserving privacy without revealing sensitive information in the social
network is a serious concern. Recent developments for preserving privacy using
anonymization techniques are focused on relational data only. Preserving
privacy in social networks against neighborhood attacks is an initiation which
uses the definition of privacy called k-anonymity. k-anonymous social network
still may leak privacy under the cases of homogeneity and background knowledge
attacks. To overcome, we find a place to use a new practical and efficient
definition of privacy called ldiversity. In this paper, we take a step further
on preserving privacy in collaborative social network data with algorithms and
analyze the effect on the utility of the data for social network analysis.
