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ICCCN 2021 : International Conference on Computing and Communication Networks, SPECIAL SESSION ON :Emerging Trends in Big Data and Edge Intelligence Privacy: Architectures and Applications


When Nov 19, 2021 - Nov 20, 2021
Abstract Registration Due Jul 30, 2021
Submission Deadline Aug 30, 2021
Notification Due Sep 30, 2021
Final Version Due Oct 20, 2021
Categories    machine learning and iot   bigdata analytics tools   data privacy and security   forensic data analytics (fda)

Call For Papers

The recent advances in Data Science, Machine Learning, Artificial Intelligence, and beyond 5G networks enabled by several deployment models ranging from centralized cloud computing
to Edge/Fog models have deeply changed business and people’s daily lives. The implementation of such techniques addresses various fields. It makes things ‘smart’ in different domains, such as Big Data, Large-scale Data Analytics, IoT, and sophisticated
Business, Healthcare, Energy, Transportation, Environment, and Industrial systems. A
common concern among all the above-mentioned technologies is security, data security. The
management of privacy-sensitive information is vital for the success of the next data-enabled era. This poses several timely questions on the sustainability of such development, guided by the advancement of emerging technologies in big data and large scale data analytics architectures, networks, frameworks, and applications.
The primary goal of this session is to provide a platform for researchers, scientists, industry experts, and scholars to share their novel ideas and research results on recent advances in data science for sustainable Data Analytics and security environment. Also, this session provides an opportunity to account for state-of-the-art works, future trends impacting information, communications, architectures, and applications, focusing on elucidating the challenges, opportunities, and inter-dependencies associated with the horizon of the data era. Authors are solicited to contribute original papers from researchers and practitioners working in privacy, security, trustworthy data systems, and related areas. Submissions that describe significant advances in Big Data, ML, AI, Blockchain, Edge/Fog, and IoT are invited to share research, best practices, and review papers.


Topics to be discussed in this special session include (but are not limited to) Architectures and Applications the following:

● AI/ML for Big Data and Edge/Fog IoT systems
● Access control for Big Data and Edge/Fog IoT systems
● Blockchain-enabled solutions for Cloud and Edge/Fog IoT systems
● Privacy in Edge Intelligence Architectures and Applications
● Privacy in Big Data and Large-scale Data analytics
● Privacy in Wireless Sensor Networks,
● Privacy in Network function virtualization
● Privacy in Software-defined networking
● Privacy Information Management
● Privacy Data Integration
● Federated machine learning
● Distributed machine learning and online learning
● Transfer learning and interactive learning for IoT systems
● Adversarial machine learning
● Privacy by Design Data Analytics
● Intelligent systems for effective detection of cyber-attacks with big data analytics
● Big data analytics for Cyber threat intelligence and detection
● Big data analytics for intrusion detection in IoT
● Big data analytics for Cloud/Edge systems security
● Malware detection using big data analytics and intelligent systems
● Forensic Data Analytics (FDA)
● Stream data processing for real-time threat analysis


Researchers and practitioners are invited to submit papers for this special theme session on [insert special session topic] on or before [insert due date]. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE CONFERENCE’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at All submitted papers will be reviewed on a double-blind, peer review basis.

NOTE: While submitting the paper in this special session, please specify [insert special session title] at the top (above paper title) of the first page of your paper.

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