k-degree anonymity is known as one of the best models for anonymizing social network graphs. Although recent works have tried to address privacy challenges graphs, levels are considered be independent features graph degree sequence. In other words, optimal value k not graph, leading increasing information loss. Additionally, may need a high level. addition, determining in advance big problem da...

The increasing popularity of graph data, such as social and online communities, has initiated a prolific research area in knowledge discovery and data mining. As more real-world graphs are released publicly, there is growing concern about privacy breaching for the entities involved. An adversary may reveal identities of individuals in a published graph, with the topological structure and/or bas...

Motivated by a strongly growing interest in anonymizing social network data, we investigate the NP-hard Degree Anonymization problem: given an undirected graph, the task is to add a minimum number of edges such that the graph becomes k-anonymous. That is, for each vertex there have to be at least k − 1 other vertices of exactly the same degree. The model of degree anonymization has been introdu...

Releasing person specific data could potentially reveal the sensitive information of an individual. kanonymity is an approach for protecting the individual privacy where the data is formed into set of equivalence classes in which each class share the same values. Among several methods, local recoding based generalization is an effective method to accomplish k-anonymization. In this paper, we pr...

The wealth of information contained in online social networks has created a demand for the publication of such data as graphs. Yet, publication, even after identities have been removed, poses a privacy threat. Past research has suggested ways to publish graph data in a way that prevents the re-identification of nodes. However, even when identities are effectively hidden, an adversary may still ...

The availability of social network data is indispensable for numerous types of research. Nevertheless, data owners are often reluctant to release social network data, as the release may reveal the private information of the individuals involved in the data. To address this problem, several techniques have been proposed to anonymize social networks for privacy preserving publications. To evaluat...

Social graphs are widely used in research (e.g., epidemiology) and business recommender systems). However, sharing these poses privacy risks because they contain sensitive information about individuals. Graph anonymization techniques aim to protect individual users a graph, while graph de-anonymization aims re-identify users. The effectiveness of algorithms is usually evaluated with metrics. it...

Recently, recommender systems have been introduced to predict user preferences for products or services. In order to seek better prediction techniques, data owners of recommender systems such as Netflix sometimes make their customers’ reviews available to the public, which raises serious privacy concerns. With only a small amount of knowledge about individuals and their ratings to some items in...

Given a large, real graph, how can we generate a synthetic graph that matches its properties, i.e., it has similar degree distribution, similar (small) diameter, similar spectrum, etc? We propose to use “Kronecker graphs”, which naturally obey all of the above properties. We present a fast linear time algorithm for fitting the Kronecker graph generation model to real networks. Experiments on la...