De-anonymizing social networks

The problem of de-anonymizing social networks is to identify the same users between two anonymized social networks [7] (Figure 1). Network de-anonymization task is of multifold significance, with user profile enrichment as one of its most promising applications. After the deanonymization and alignment, we can aggregate and enrich user profile information from different online networking services and make the bundled profiles available for end-users as well as thirdparty applications. In our project, we aim to develop effective algorithms for de-anonymizing real-world social networks. Specifically, we focus on two tasks: one is to align the networks of Flickr1 and Instagram2 and the other is to align Flickr and Twitter 3. Our work is motivated by the two parts of information that network data is composed of: network structure and node attributes. Preliminary tests have shown that de-anonymizing algorithm based merely on node attributes, e.g. user names, is computationally efficient but not satisfactorily accurate. On the other hand, algorithms that rely on network structures, which bring in more relationship information, may contribute to the precision of de-anonymization. However, not only may the structure of the real-world social networks be quite different, but also the computation costs will be intractably high since the maximum common