The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates embedding methods, aiming at learning a continuous vector space for the which is amenable to be adopted in traditional machine algorithms favor representations. Graph methods build an important bridge between social network analysis and analytics as networks naturally generate unprecedented volume co...

An increasing amount of data are becoming publicly available over the Internet. These data are released after applying some anonymization techniques. Recently, researchers have paid significant attention to analyzing the risks of publishing privacy-sensitive data. Even if data anonymization techniques were applied to protect privacy-sensitive data, several de-anonymization attacks have been pro...

The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Anonymization of these social graphs is important to facilitate publishing these data sets for analysis by external entities. Prior work has concentrated mostly on node identity anonymization and structural anonymization. But with the growing interest in analyzing social...

The risks of publishing privacy-sensitive data have received considerable attention recently. Several deanonymization attacks have been proposed to re-identify individuals even if data anonymization techniques were applied. However, there is no theoretical quantification for relating the data utility that is preserved by the anonymization techniques and the data vulnerability against de-anonymi...

We study the computational complexity of k-anonymizing a given graph by as few graph contractions as possible. A graph is said to be k-anonymous if for every vertex in it, there are at least k − 1 other vertices with exactly the same degree. The general degree anonymization problem is motivated by applications in privacy-preserving data publishing, and was studied to some extent for various gra...

We tackle the problem of user de-anonymization in social networks characterized by scale-free relationships between users. The network is modeled as a graph capturing the impact of power-law node degree distribution, which is a fundamental and quite common feature of social networks. Using this model, we present a de-anonymization algorithm that exploits an initial set of users, called seeds, t...

As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of COVID-19 pandemic. Today, OSNs become a core part many people’s daily lifestyles. Therefore, increasing dependency on encourages privacy requirements to protect from malicious sources. contain sensitive information about each end user that intruders may try leak for commercial ...

In this paper a comparison is performed on two of the key methods for graph anonymization and their behavior is evaluated when constraints are incorporated into the anonymization process. The two methods tested are node clustering and node modification and are applied to online social network (OSN) graph datasets. The constraints implement user defined utility requirements for the community str...

Graph clustering is widely used in many data analysis applications. In this paper we propose several parallel graph clustering algorithms based on Monte Carlo simulations and expectation maximization in the context of stochastic block models. We apply those algorithms to the specific problems of recommender systems and social network anonymization. We compare the experimental results to previou...

Edges in a social graph may represent private information that needs to be protected. Due to their graph partition schemes, existing edge anonymization methods have several drawbacks, such as high utility loss and high computational overhead. In this paper, we present a new edge anonymization method, which partitions a graph using a new vertex equivalence relation called the neighbor-set equiva...