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IEEE Internet of
Things Journal
Special Issue on
Privacy and
Security in Distributed Edge Computing and Evolving IoT
Scheduled
Publication Time: 2020
Overview
Recent advances in artificial
intelligence, edge computing, and big data, have enabled extensive
reasoning capabilities at the edge of the network. Edge servers are now
capable of extracting meaningful analytics from IoT nodes, which give
insights about unprecedented changes of data-driven economy that finds
applications in diverse sectors ranging from smart manufacturing and smart
transportation to predictive maintenance and precision healthcare. Despite
this ongoing advancement, there are growing concerns regarding the privacy
of data providers when they grant edge applications direct access to their
embedded sensors.
Data mining on genuine data could be
harmful to data privacy. For example, data mining on time-series data taken
from motion sensors, microphones, and GPS sensors could reveal users'
activities, demographics, attributes and daily interactions. This could
potentially lead to security/privacy concerns in many participatory and
opportunistic crowd-sensing applications, where a large group of
individuals having mobile devices capable of sensing and computing
collectively share data and extract information to measure, map, analyze, estimate or infer any processes of common
interest. While privacy preserving has not been the initial focus of
traditional data analytics on edge servers, when used in domains such as
cyber security, there are incentivized, malicious adversaries present in
the system willing to game and exploit edge processing vulnerabilities.
This special issue focuses on
solutions that leverage techniques and insights from the domains of
artificial intelligence, edge computing, and big data to resolve privacy
and security challenges in distributed edge computing and evolving IoT
applications. Topics of interest for this special issue include, but are
not limited to
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Optimization of the utility-privacy tradeoffs
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Privacy-preserving data aggregation
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Privacy-preserving data mining
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Privacy protection in edge computing assisted with
evolving IoT
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Privacy preserving solutions for crowdsensing
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Privacy preserving in presence of advanced persistent
threats
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Privacy-enhancing cryptographic techniques
·
Multiparty access control in edge computing assisted
with evolving IoT
·
Middleware for privacy protection in IoT applications
·
Future perspectives of privacy issues in IoT
applications
Timeline
Submissions Deadline: July 1, 2019 July 31,
2019 [extended]
First Reviews Due: September 15, 2019
Revision Due: October
15, 2019
Second Reviews Due/Notification: November 15, 2019
Final Manuscript Due: December 15, 2019
Publication Date: 2020
Submission
All original manuscripts or revisions to the IEEE IoT
Journal must be submitted electronically through IEEE Manuscript Central,
http://mc.manuscriptcentral.com/iot. Solicited original submissions must
not be currently under consideration for publication in other venues.
Author guidelines and submission information can be found at
http://iot.ieee.org/journal.
Guest Associate Editors
Dr. Alireza Jolfaei, Macquarie
University, Australia (alireza.jolfaei@mq.edu.au)
Dr. Pouya Ostovari, San Jose State
University, USA (pouya.ostovari@sjsu.edu)
A/Professor Mamoun Alazab, Charles Darwin University,
Australia (mamoun.alazab@cdu.edu.au)
Professor Iqbal Gondal, Federation University,
Australia (iqbal.gondal@federation.edu.au)
Professor Krishna Kant, Temple University, USA
(kkant@temple.edu)
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