The publication of increasing amounts of anonymised open source data has resulted in a
worryingly rising number of successful re-identification attacks. This has a number of privacy …

Novel attacks on dataset privacy are usually met with the same range of responses: surprise
that a route to information gain exists from information previously thought to be safe; …

Online social networks (OSNs) often contain sensitive information about individuals.
Therefore, anonymizing social network data before releasing it becomes an important issue …

Social network data is typically made available in a graph format, where users and their
relations are represented by vertices and edges, respectively. In doing so, social graphs …

In order to prevent the disclosure of privacy-sensitive data, such as names and relations
between users, social network graphs have to be anonymised before publication. Naive …

Previous works on social network de-anonymization focus on designing accurate and
efficient de-anonymization methods. We attempt to investigate the intrinsic relationship …

The characterisation of vertices in a network, in relation to other peers, has been used as a
primitive in many computational procedures, such as node localisation and (de-) …

Social networks allow their users to make their profiles and relationships private. However,
in recent years several powerful de-anonymization attacks have been proposed that are …

Social networks have an important and possibly key role in our society today. In addition to
the benefits, serious privacy concerns also emerge: there are algorithms called de …

Active re-identification attacks constitute a serious threat to privacy-preserving social graph
publication, because of the ability of active adversaries to leverage fake accounts, aka sybil …