A data driven anonymization system for information rich online social network graphs

• Considers a new approach for the anonymization of complex social graph data.
• Preserves local social neighborhood of nodes during anonymization.
• Has an integrated synthetic generator for online social network graph data.
• Strong privacy level for complex graph data using k-anonymity and t-closeness.
• Results show a lower anonymization cost than other methods.

In recent years, online social networks have become a part of everyday life for millions of individuals. Also, data analysts have found a fertile field for analyzing user behavior at individual and collective levels, for academic and commercial reasons. On the other hand, there are many risks for user privacy, as information a user may wish to remain private becomes evident upon analysis. However, when data is anonymized to make it safe for publication in the public domain, information is inevitably lost with respect to the original version, a significant aspect of social networks being the local neighborhood of a user and its associated data. Current anonymization techniques are good at identifying risks and minimizing them, but not so good at maintaining local contextual data which relate users in a social network. Thus, improving this aspect will have a high impact on the data utility of anonymized social networks. Also, there is a lack of systems which facilitate the work of a data analyst in anonymizing this type of data structures and performing empirical experiments in a controlled manner on different datasets. Hence, in the present work we address these issues by designing and implementing a sophisticated synthetic data generator together with an anonymization processor with strict privacy guarantees and which takes into account the local neighborhood when anonymizing. All this is done for a complex dataset which can be fitted to a real dataset in terms of data profiles and distributions. In the empirical section we perform experiments to demonstrate the scalability of the method and the improvement in terms of reduction of information loss with respect to approaches which do not consider the local neighborhood context when anonymizing.