A conceptual framework for privacy preservation of released social network data considering relational dynamic anonymisation

The diversity and richness of information available in online social networks have created a great incentive for social network providers to publish or share the social network data to third parties. The released information may contain sensitive information about the individuals which could be used unethically by the adversaries making it essential for the providers to preserve the confidentiality of the social network data before release. The framework presented in this paper has been formulated with a view to help service providers in selecting the appropriate privacy preservation technique of social network data based on its intended usage. Most of the privacy preservation techniques are static in nature and tend to ignore the equally important privacy breach issue due to multiple releases. The proposed framework has considered both the static and dynamic anonymisation. As the dynamic anonymisation of social network data is still in infancy stage, the authors propose the use of relational dynamic anonymisation techniques for preserving privacy in a dynamic context.