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Online Social Network (OSN) data holders like Facebook, Twitter, Linked-In release their data to third parties such as researchers, data mining practitioners etc. Third parties mine the released data and help data holders gain deeper insights about the network. Releasing the social network graph in its actual form results in loss of privacy. As a result OSN users could end up losing trust that they...
In this paper, we tackle the problem of graph generalization in the context of privacy-preserving social network mining. By grouping together nodes that are not only similar but that also belong to the same k-shells, we better preserve the community structure of the graph, its utility in case of clustering-related applications, while still achieving some privacy level through the concept of graph...
Although a lot of literatures have been proposed on the issue of privacy preserve with relational data, social networks bring new challenges of resisting re-identify attacks. Based on message passing, an approach of privacy preserve in social networks is proposed in this paper. Individuals are assigned to different clusters according to their quasi-identifies and structural similarity measured by...
The(P, α, K) anonymity model for privacy protection of personal information in the social networks is proposed in this paper. The hidden fields P and the hidden levels a are set according to theindividual privacy needs of the users. Then make the released data to meet the privacy protection requirements through the Datafly algorithm and the clustering algorithm. The experimental data shows that the...
Online Social Networks (OSNs) are becoming more important in the web 2.0 paradigm. Although most implementations of OSN are not distributed applications, users conforming an OSN work autonomously posting their information in the OSN and interacting among them. Users are responsible of the information they post in their profile and, in the vast majority of social networks, they can limit the disclosure...
The flow of information in the network currently existing attacks and malicious damage, a privacy protection algorithm based on the maximum greedy is proposed, the data privacy acts as weights between nodes in network diagram , change these weights to achieve the protection of important data, by obtaining the shortest path way to ensure that efficiency is not influenced ,the experimental results show...
In a ubiquitously connected world, social networks are playing an important role on the Internet by allowing users to find groups of people with similar interests. The data needed to construct such networks may be considered sensitive personal information by the users, which raises privacy concerns. The problem of building social networks while user privacy is protected is hence crucial for further...
Sharing personal information and documents is pervasive in Web 2.0 environments, which creates the need for properly controlling shared data. Most existing authorization and policy management systems are for organizational use by IT professionals. Average Web users, however, do not have the sophistication to specify and maintain privacy policies for their shared content. In this paper, we aim to utilize...
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