Data Privacy in Social Networks

Dear Colleagues,

As online social networks (OSNs) are becoming the most important medium in which to exchange information, ideas, opinions, and different kinds of content in our societies, they also generate a huge amount of data which may contain, or allow the inference of, sensitive personal information, which is protected by legislation and regulations in several world jurisdictions. At the same time, such data may be put to use in data analysis that provides desirable societal outcomes, facilitating the provision of novel services, understanding of human behavior, and emergency response. Therefore, a tension between privacy and utility emerges.

This tension calls for research that resolves the tradeoff in a satisfactory manner: providing strong, robust, and intelligible privacy guarantees and guidelines, while at the same time allowing for privacy-preserving data analysis, data mining, and data science outcomes for the social good. There has been progress in these fronts, but several challenges remain unresolved and imminently call for further research that leverages recent advances in social network analysis, network science, privacy-preserving technologies, and mathematical privacy models, so as to provide users and organization with both the benefits the data science delivers and the privacy safeguards that they desire.

This Special Issue on “Privacy Protection in Social Network Data” aims to promote new theories, techniques, principles, tools, and methods with which to better resolve the tradeoff between personal privacy safeguards and the affordances of data analysis in the realm of social network data. Potential topics include but are not limited to the following:

• Privacy-preserving social network data publishing;
• Privacy-preserving data mining on social network data;
• Privacy guarantees for network data;
• Personalized privacy and group privacy in social network data;
• Parallel and distributed architectures for social network data privacy techniques;
• Legislation, ethics, and human-in-the-loop aspects of social network data privacy;
• Social network embeddings with privacy-aware characteristics;
• Privacy in attributed, heterogeneous, and tagged social network data;
• Privacy-aware recommendation systems over social network data;
• Privacy enhancements of social network applications.

Dr. Panagiotis Karras
Guest Editor

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• Privacy preservation
• Social networks
• Confidentiality
• Anonymity