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Privacy Preservation for Large-Scale User Data in Social Networks

Submission Deadline: 31 December 2017

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:

  • Privacy aware user data collection techniques for large-scale social networks
  • Privacy preserving user data storage and management for large-scale social networks
  • Privacy conserving data fusion and transformation algorithms and techniques for large-scale social networks
  • Privacy conservation applications and implementations on user data in large-scale social networks
  • Privacy sensitive data sharing and visualization for large-scale social networks
  • Privacy aware user data access control for large-scale social networks
  • Privacy preserving user data analytics, processing, and mining for large-scale social networks
  • Privacy and service trade-off for large-scale mobile social network user data
  • Business models and standards for privacy perseveration of large-scale social network user data
  • Blockchain theories, models and applications in large-scale social networks

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor: Yuan Gao, Tsinghua University, China

Guest Editors:

  1. Zhipeng Cai, Georgia State University, USA
  2. Liran Ma, Texas Christian University, USA
  3. Yunchuan Sun, Beijing Normal University, China
  4. Matevz Pustisek, University of Ljubljana, Slovenia
  5. Su Hu, University of Electrical Science and Technology of China, China
  6. Yi Li, Renmin University of China, China

 

Relevant IEEE Access Special Sections:

  1. Convergence of Sensor Networks, Cloud Computing, and Big Data in Industrial Internet of Things
  2. Curbing Crowdturfing in Online Social Networks
  3. Advanced Data Analytics for Large-scale Complex Data Environments

 

IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:
http://mc.manuscriptcentral.com/ieee-access

For inquiries regarding this Special Section, please contact: specialsections@ieee.org