IEEE Computational Intelligence Magazine - August 2022 - 2

CIM Editorial Board
Editor-in-Chief
Chuan-Kang Ting
National Tsing Hua University
Department of Power Mechanical Engineering
No. 101, Section 2, Kuang-Fu Road
Hsinchu 30013, TAIWAN
(Phone) +886-3-5742611
(Email) ckting@pme.nthu.edu.tw
Chuan-Kang Ting
National Tsing Hua University, TAIWAN
Editor's
Editor's
Remarks
Founding Editor-in-Chief
Gary G. Yen, Oklahoma State University, USA
Past Editors-in-Chief
Kay Chen Tan, Hong Kong Polytechnic
University, HONG KONG
Hisao Ishibuchi, Southern University of Science and
Technology, CHINA
Editors-At-Large
Piero P. Bonissone, Piero P. Bonissone Analytics,
USA
David B. Fogel, Natural Selection, Inc., USA
Vincenzo Piuri, University of Milan, ITALY
Marios M. Polycarpou, University of Cyprus,
CYPRUS
Jacek M. Zurada, University of Louisville, USA
Associate Editors
Sansanee Auephanwiriyakul, Chiang Mai
University, THAILAND
Keeley Crockett, Manchester Metropolitan
University, UK
Liang Feng, Chongqing University, CHINA
Jen-Wei Huang, National Cheng Kung University,
TAIWAN
Eyke Hüllermeier, University of Munich, GERMANY
Fakhri Karray, University of Waterloo, CANADA
Sheng Li, University of Georgia, USA
Hsuan-Tien Lin, National Taiwan University, TAIWAN
Hongfu Liu, Brandeis University, USA
Zhen Ni, Florida Atlantic University, USA
Nelishia Pillay, University of Pretoria, SOUTH AFRICA
Danil Prokhorov, Toyota R&D, USA
Kai Qin, Swinburne University of Technology,
AUSTRALIA
Manuel Roveri, Politecnico di Milano, ITALY
Gonzalo A. Ruz, Universidad Adolfo Ibáñez,
CHILE
Ming Shao, University of Massachusetts
Dartmouth, USA
Kyriakos G. Vamvoudakis, Georgia Tech, USA
Handing Wang, Xidian University, CHINA
Dongrui Wu, Huazhong University of Science
and Technology, CHINA
Bing Xue, Victoria University of Wellington,
NEW ZEALAND
Dongbin Zhao, Chinese Academy of Sciences,
CHINA
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Digital Object Identifier 10.1109/MCI.2022.3180606
Digital Object Identifier 10.1109/MCI.2022.3180649
Date of current version: 19 July 2022
2 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2022
T
he employment of machine learning often requires a large
amount of data, including our sensitive and personal information,
to train models for various applications. With
growing worldwide concerns over data privacy, particularly with
the recent introduction of the EU's General Data Protection
Regulation (GDPR), it has become an important topic for AI
research and application to implement data privacy-preserving
technologies. Researchers or companies that utilize personal data in their AI models
face the pressure and difficulty of balancing the advancement of AI and data privacy.
In the quest for such a balance, machine learning technologies that conform to
some particular aspects of data protection regulated by the GDPR, often combining
privacy-preserving technologies such as homomorphic encryption, differential privacy,
secure multiparty computation, or federated learning, are used to protect data
and models from being accessed by other parties.
Two Feature articles in this issue give us examples of privacy-preserving technologies.
The first article presents an introduction to deep learning based on homomorphic
encryption for guaranteeing the privacy of user data. The second article proposes
a decentralized federated learning framework assisted by blockchain to avoid information
leakage from malicious clients. As demanding as complying with regulations may
seem, privacy preserving will build trust between consumers and AI systems, further
increasing the use and opportunity of AI.
The Columns comprises two articles about multi-objective optimization (MOO).
The first article proposes the CSD indicator that assesses the convergence, sparsity, and
diversity of solutions for sparse MOO. It compares the CSD values of 11 state-of-the
art multi-objective evolutionary algorithms (MOEAs) on 60 test problems. The second
article formulates pandemic containment as an MOO problem considering
infection peaks, economic damage, and containment cost, and deals with the problem
using MOEAs.
In the new Industrial and Governmental Activities column, the VP-IGA invites
industry leaders to participate in workshops, conferences, and panels for the connection
and collaboration between CIS and the industry. In this issue, we interview Yongduan
Song, the new Editor-in-Chief of IEEE Transactions on Neural Networks and
Learning Systems, who talks about this publication, his life and favorites. We hope you
will enjoy the articles in this issue. Please do not hesitate to contact me at ckting@
pme.nthu.edu.tw if you wish to give any feedback for our magazine.
Quest for the Balance of AI and Privacy
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IEEE Computational Intelligence Magazine - August 2022

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - August 2022
