In recent decades, social network anonymization has become a crucial research field due to
its pivotal role in preserving users' privacy. However, the high diversity of approaches …

Privacy on social networks is one of the most important and well-known issues. Various
algorithms have been proposed to preserve the privacy of social network, all of which try to …

There are plenty of applications that use graphs for representing the association between
different entities. Many research communities are interested in publishing such graphs to …

We present a novel method for publishing differentially private synthetic attributed graphs.
Unlike preceding approaches, our method is able to preserve the community structure of the …

Our private connections can be exposed by link prediction algorithms. To date, this threat
has only been addressed from the perspective of a central authority, completely neglecting …

k-degree anonymity is known as one of the best models for anonymizing social network
graphs. Although recent works have tried to address the privacy challenges of social …

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 …

A k-truss is a graph such that each edge is contained in at least k-2 triangles. This notion has
attracted much attention, because it models meaningful cohesive subgraphs of a graph. We …

Social network data publishing is dynamic, and attackers can perform association attacks
based on social network directed graph data at different times. The existing social network …

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 …