Privacy Preserving Approach in Dynamic Social Network Data Publishing

Abstract

In recent years, social networks have gained special attention to share information and to maintain a relationship with other people. As the data produced from such platforms are being analyzed, the privacy preservation methods must be applied before making the data publicly available. The anonymization techniques consider one-time releases and do not re-publish the dynamic social network data. The relationship between individuals changes with time so it may breach user privacy in dynamic social networks. In this paper, we propose an anonymization approach to preserve the user identity from all the published time-series dataset of a social network.