We define and analyze an anonymization problem in undirected graphs, which is motivated by certain privacy issues in social networks. The goal is to remove a small number of vertices from the graph such that in the resulting subgraph every occurring vertex degree occurs many times. We prove that the problem is NP-hard for trees, and also for a number of other highly structured graph classes. Fu...

Anonymity is one the most important problems that emerged with increasing number of graph-based social networks. It not straightforward to ensure anonymity by adding or removing some nodes from graph. Therefore, a more sophisticated approach required. The consideration degree in graph may facilitate having knowledge about specific nodes. To handle this problem, prominent solutions k-degree anon...

The recent rise in popularity of social networks, such as Facebook and MySpace, has created large quantities of data about interactions within these networks. Such data contains many private details about individuals so anonymization is required prior to attempts to make the data more widely available for scientific research. Prior work has considered simple graph data to be anonymized by remov...

In this paper we study privacy preservation for the publication of search engine query logs. In particular, we introduce a new privacy concern, which is that of website privacy (or business privacy). We define the possible adversaries that could be interested in disclosing website information and the vulnerabilities found in the query log, from which they could benefit. In this work we also det...

Protecting the identities of the actors along with their sensitive information has become a matter of concern for the organizations which are publishing huge amounts of data every day for the purpose of research. Recent studies have shown that simply removing the sensitive labels associated with the actors do not guarantee their privacy protection. The structural property of the graph associate...

Recently, several anonymization algorithms have appeared for privacy preservation on graphs. Some of them are based on randomization techniques and on k-anonymity concepts. We can use both of them to obtain an anonymized graph with a given k-anonymity value. In this paper we compare algorithms based on both techniques in order to obtain an anonymized graph with a desired k-anonymity value. We w...

In this paper, de-anonymizing internet users by actively querying their group memberships in social networks is considered. In this problem, an anonymous victim visits the attacker’s website, and the attacker uses the victim’s browser history to query her social media activity for the purpose of de-anonymization using the minimum number of queries. A stochastic model of the problem is considere...

Though representing a promising approach for personalization, targeting, and recommendation, aggregation of user profiles from multiple social networks will inevitably incur a serious privacy leakage issue. In this paper, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS) aiming at de-anonymizing heterogeneous social networks. NHDS firstly leverages the network graph structure to s...

Data anonymization is used to remove user specific information from published data sets. Different kinds of anonymization techniques are used to eliminate various types of attacks. Anonymization process modifies the data into human unidentifiable form and it is most efficient than any other privacy preserving techniques like encryption etc. Encryption is costly when compared to anonymization as...

Motivated by a strongly growing interest in graph anonymization, we study the NP-hard Degree Anonymity problem asking whether a graph can be made k-anonymous by adding at most a given number of edges. Herein, a graph is k-anonymous if for every vertex in the graph there are at least k−1 other vertices of the same degree. Our algorithmic results shed light on the performance quality of a popular...