Ambiguity in Social Network Data for Presence, Sensitive-Attribute, Degree and Relationship Privacy Protection.

ABSTRACT: Maintaining privacy in network data publishing is a major challenge. This is because known characteristics of individuals can be used to extract new information about them. Recently, researchers have developed privacy methods based on k-anonymity and l-diversity to prevent re-identification or sensitive label disclosure through certain structural information. However, most of these studies have considered only structural information and have been developed for undirected networks. Furthermore, most existing approaches rely on generalization and node clustering so may entail significant information loss as all properties of all members of each group are generalized to the same value. In this paper, we introduce a framework for protecting sensitive attribute, degree (the number of connected

SUBMITTER: Rajaei M 

PROVIDER: S-EPMC4481469 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature