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. Multiple instances of the social network may allow the adversary to identify the user by joining the information together. The existing anonymization methods for a single instance of a social network are not enough to preserve user privacy across multiple instances. Moreover, it requires all instances together for the social graph anonymization process. We proposed a method that anonymizes the current instance of the social graph and publishes it as soon as the instance is available. The proposed anonymization technique modifies the current social graph irrespective of further instances. The average relative error calculates the deviation in query results for different privacy levels. The experimental results highlight that the proposed approach generates fewer dummy nodes.