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TNSE:SI-SPSN 2017 : IEEE Transactions on Network Science and Engineering Special Issue on Scalability and Privacy in Social Networks

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Link: https://www.computer.org/cms/Computer.org/transactions/cfps/cfp_tnsesi_spsn.pdf
 
When N/A
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Submission Deadline Sep 1, 2017
 

Call For Papers

CALL FOR PAPERS
IEEE Transactions on Network Science and Engineering
Special Issue/Section on Scalability and Privacy in Social Networks

GUEST EDITORS:
Donghyun Kim (Lead), Kennesaw State University, Marietta, GA, USA. Email: donghyun.kim@kennesaw.edu
My T. Thai, University of Florida, Gainesville, FL, USA. Email: mythai@cise.ufl.edu
R. N. Uma, North Carolina Central University, Durham, NC, USA. Email: ruma@nccu.edu

TOPIC SUMMARY:
The growing popularity of Online Social Networks and their emerging applications attracted much attention from both academia and industry. 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 a mathematical model such as a graph, which is usually very large, that can later be used as an input to other algorithms for further processing. Recent reports show that if the abstractions of social networks are not properly designed, a large amount of private information can be extracted from them. As the area of Data Science and related technologies are getting more mature, it is highly possible that what is considered a safe abstraction of social networks today, becomes unsafe tomorrow. Unfortunately, the problem of designing privacy-aware social network abstractions is very challenging. Generally speaking, this is because a change in input data forces a change in the structure of the algorithms which will process the input data. Such change can also affect the output of the algorithm. Certainly, the emerging Big Data analytic techniques, such as differential analysis, will bring more complexity to this already-conundrum-like problem. Most importantly, any solution to this problem has to be scalable. This special issue aims to provide a prime venue for researchers from both academia and industry to discuss about this impelling, but not well-understood, problem.
The topics of interest for this special issue include, but are not limited to:
• Security and privacy in online social networks
• Privacy preserving data mining and machine learning for social systems
• Differential privacy in social networks
• Trust and reputations in social systems
• Detection, analysis, prevention of spam, phishing, and misbehavior in social systems
• Privacy preserving techniques and its efficiency
• Privacy in crowdsourcing systems
• Trust and reputations in social systems
• Modeling new social networks and relevant privacy issues
• Novel social applications/systems and related scalability/privacy issues

IMPORTANT DATES:
Closed for submissions: 09/01/2017
Results of first round of reviews: 12/01/2017
Submission of revised manuscripts: 01/01/2018
Results of second round of reviews: 02/05/2018
Publication materials due: 03/19/2018

SUBMISSION GUIDELINES:
Prospective authors are invited to submit their manuscripts electronically after the “open for submissions” date, adhering to the IEEE Transactions on Network Science and Engineering guidelines (http://www.computer.org/portal/web/TNSE/author). Please submit your papers through the online system (https://mc.manuscriptcentral.com/TNSE-cs) and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal. If requested, abstracts should be sent by e-mail to the Guest Editors directly.

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