Special Issue on Privacy Preserved Learning in Distributed Communication Systems

Published 21 April, 2021

Recently, we have witnessed wide use of machine learning techniques in large-scale, distributed communication systems. These techniques empower the development of various intelligent applications, such as face recognition by distributed cameras, healthcare in Internet of Things (IoT) networks and object detection by moving vehicles/drones.

However, concerns over security and privacy, especially the risk of data leakage, have proved critical barriers for extensive applications of machine learning in distributed communication systems. For example, the data curators collecting information from participating users may not be reliable. In addition, systems could be hacked. Given the complexity and scale of modern distributed communication systems, innovative research is urgently required to improve existing privacy protections, and discover new mathematical tools and techniques. This special issue will feature theoretical foundations and empirical studies on data privacy in distributed communication systems.

Topics Covered:

Areas of interest include, but are not limited to:

  • Privacy leakage and preservation in mobile/edge/fog computing
  • Privacy leakage and preservation in vehicular/drone networks
  • Privacy leakage and preservation in IoT networks
  • Privacy leakage and preservation in CDN (content distribution networks) and ICN (information centric networks)
  • Privacy leakage and preservation for smart internet routing
  • Privacy leakage and preservation in ad-hoc networks
  • Privacy leakage and preservation in location-based services
  • Data poison for learning in distributed systems
  • Embedding federated/distributed learning in distributed communication systems
  • Gradient/parameter leakage for learning in distributed systems
  • Differential attacks and other attacks to crack data privacy in distributed communication systems
  • Differential privacy mechanism design for distributed communication systems
  • Data anonymisation in distributed communication systems
  • Homomorphic encryption and privacy protection for learning in distributed communication systems
  • Multi-party computing and privacy protection in distributed communication systems
  • Blockchain and privacy protection in distributed communication systems

Important Deadlines (Tentative):

  • Submission deadline: 30 September 2021
  • First notification: 15 October 2021
  • Revised version deadline: 1 December 2021
  • Final notification: 15 February 2022
  • Publication date: April 2022

Submission Instructions:

Please read the Guide for Authors before submitting. All articles should be submitted online via the editorial management system. Please select the option “SI: Privacy and Learning”.

Guest Editors:

Brief Biographies of Guest Editors:

  • Yipeng Zhou is a lecturer in computer science with the Department of Computing at Macquarie University and the recipient of ARC DECRA in 2018. He has been a research fellow with the Institute for Telecommunications Research (ITR) at the University of South Australia, a lecturer with the College of Computer Science and Software Engineering, Shenzhen University, China, and a postdoctoral fellow at the Institute of Network Coding (INC) at The Chinese University of Hong Kong (CUHK).
  • Keshav Sood is currently a lecturer at the Centre for Cyber Security and Innovation (CSRI) at Deakin University in Melbourne, and is deputy director of its Bachelor of Cyber Security course. He previously worked as a research fellow at the Advanced Cyber Security Engineering Research Centre  (ACSRC) at  the University of Newcastle, Australia.  He has also worked as a mentor for an online FutureLearn course on cyber security.  He is a professional engineer and his research interests include software-defined networks, Internet of Things and cyber security in next generation networks. He has published in a number of IEEE titles.
  • Abderrahim Benslimane has been a full professor of Computer Science at Avignon University in France since 2001. He is vice dean of the STS Faculty and head of the master’s degree SICOM. He was nominated IEEE VTS Distinguished Lecturer and IEEE ComSoc VC of Multimedia TC 2020-22. He is past Chair of the ComSoc Technical Committee of Communication and Information Security 2017-19. He is EiC of Inderscience Int. J. of Multimedia Intelligence and Security (IJMIS) and is involved editorially in a range of other publications. He is co-founder and serves as general chair of the IEEE WiMob and of iCOST and MoWNet international conference. He has more than 210 refereed international publications and more than 16 special issues.
  • Shui Yu is a Professor at the School of Computer Science, University of Technology Sydney. His research interests include big data, security and privacy, networking and mathematical modelling. He has published three monographs and edited two books and contributed to more than 400 technical papers, many of which have appeared in top journals and conferences. His h-index is 53. Dr. Yu initiated the research field of networking for big data in 2013, and his research outputs have been widely adopted by industrial systems, such as Amazon cloud security. He currently serves on a number of prestigious editorial boards, is a senior member of IEEE, a member of AAAS and ACM, and a distinguished lecturer of the IEEE Communications Society.

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