Submission Deadline: 31 December 2017
IEEE Access invites manuscript submissions in the area of Privacy Preservation for Large-Scale User Data in Social Networks.
Social networks have become one of the most popular platforms for people to communicate and interact with their friends, and share personal information and experiences (e.g., Facebook has over 1.23 billion monthly active users). The increasing popularity of social networks has generated extremely large-scale user data (e.g., Twitter generates 500 million tweets per day and around 200 billion tweets per year). These data can help improve people’s quality of life as well as benefit various interest groups (e.g., advertisers, application developers, and data-mining researchers). Given the huge amount of user data and relations available in social networks, however, privacy may be compromised if learning algorithms are used on the released data to infer undisclosed private information. Hence, user data privacy preservation has become one of the most urgent research issues in social networks.
A great deal of effort has been devoted to protecting user data privacy. Aside from cryptography and security protocols, there has been work on enforcing industry standards such as the platform for privacy preferences project (P3P) and government policies (e.g., HIPAA regulations) to grant individuals control over their own privacy. These existing techniques and policies aim to block the direct disclosure of private information to a certain extent. However, these still lack techniques that can effectively prevent the indirect disclosure of privacy in social networks, which can be achieved by intelligently combining large-scale seemingly innocuous or unrelated user data. Therefore, effective privacy protection techniques and tools are actively sought to prevent malicious inference of private information. These privacy preserving techniques and tools are required to work over the heterogeneous composition of diverse hardware, operating systems, and network domains.
This Special Section in IEEE Access solicits high-quality contributions that focus on the design and development of novel algorithms, technologies, and tools to address the privacy issues towards large-scale user data in social networks. The topics of interest include, but are not limited to:
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Associate Editor: Yuan Gao, Tsinghua University, China
Relevant IEEE Access Special Sections:
IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland
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