Guest Editorial Introduction to the Special Section on Scalability and Privacy in Social Networks

• Published in: IEEE transactions on network science and engineering Vol. 7; no. 2; pp. 843 - 844
• Main Authors: Kim, Donghyun, Thai, My T., Uma, R. N.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Approximation algorithms; Big Data; Crowdsensing; Data privacy; Privacy; Scalability; Social networking (online); Social networks; Special issues and sections
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2019.2959674

The papers in this special section focus on scalability and privacy in online social network services. (OSNs) The growing popularity of OSNs and their emerging applications attracted much attention from both academia and industry during recent years. Due to their nature, social networks are considered as sources of Big Data containing large amounts of privacy-sensitive information. A social network is frequently abstracted using mathematical tools, especially graph models, which are usually very large. As a result, it is of great importance to continuously improve the performance. These papers aim to collect recent progresses on these two important subjects, which are frequently co-related, and promotes the discussions about them. We appreciate contributions to this special section and the valuable and extensive efforts of the reviewers. The topics of this special section cover efficient rumor blocking on social networking, privacy issue on mobile crowdsensing, a new inference attack against social network, a scalable data publication scheme with user privacy protection, and a new random matrix-based approach to publish online social network graph with privacy protection.