Wireless Communications and Mobile Computing

Privacy-Preserving Techniques in Deep Learning for Mobile Computing


Publishing date
01 Nov 2021
Status
Closed
Submission deadline
25 Jun 2021

Lead Editor
Guest Editors

1Guangzhou University, Guangzhou, China

2University of Illinois Springfield, Springfield, USA

3Chinese Academy of Sciences, Beijing, China

This issue is now closed for submissions.

Privacy-Preserving Techniques in Deep Learning for Mobile Computing

This issue is now closed for submissions.

Description

In the past few years, deep learning has achieved great breakthroughs in computer vision, speech recognition, and many other areas. To support the training of deep learning, large datasets are collected from different entities in the real world. Along with tremendous efforts devoted to mobile computing, the high communication efficiency in current technology such as 5G and the strong calculation power in various mobile terminals have drawn great attention from distributed learning.

In the big data background, how to exploit potentialities from the distributed deep learning with strong mobile computation has become an important and meaningful issue to discuss. Meanwhile, extensive usages training data has also raised great concerns about data privacy. Although many privacy-preserving techniques have offered solutions to protect our personal data, many still have practical limitations (e.g., not effective or efficient enough, less user-centric). As a result, there is an urgent need for innovative privacy-preserving techniques to adequately explore the big data vs. privacy dilemma in deep learning.

This Special Issue aims to advance privacy technologies and methodologies in deep learning and further promote research activities in large-scale data-based service. The Special Issue seeks original theory- and application-driven studies to address some emerging issues and challenges from the perspective of privacy-preserving deep learning and its applications in areas such as natural language processing, computer vision, speech recognition, and so on. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

  • Deep learning methods and architecture based on mobile computation
  • Privacy-preserving crowdsensing systems for mobile computing
  • Private information storage, aggregation, and retrieval in mobile computing
  • Privacy-enhanced deep learning for mobile computing, such as federated learning, etc.
  • Blockchain-based deep learning techniques for privacy-preserving of mobile computing
  • Privacy of deep learning in different big data contexts such as online social networks, healthcare, IoT, and e-government
  • Individual privacy-preserving methods in distributed deep learning for mobile computing
  • Evaluation mechanism of models in distributed deep learning with multiple terminals
  • Privacy computing theory and language for mobile computation
  • Auditable guarantee in cryptography-based privacy-preserving methods for mobile computing
  • Incentives in game theory for the trade-off between privacy and accuracy, utility, reliability, and fault-tolerance in deep learning for mobile computing

Articles

  • Special Issue
  • - Volume 2022
  • - Article ID 2375702
  • - Research Article

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET)

Masoud Abdan | Seyed Amin Hosseini Seno
  • Special Issue
  • - Volume 2021
  • - Article ID 8745897
  • - Review Article

Key Research Issues and Related Technologies in Crowdsourcing Data Collection

Yunhui Li | Liang Chang | ... | Tianlong Gu
  • Special Issue
  • - Volume 2021
  • - Article ID 1690669
  • - Research Article

GCNRDM: A Social Network Rumor Detection Method Based on Graph Convolutional Network in Mobile Computing

Dawei Xu | Qing Liu | ... | Jian Zhao
  • Special Issue
  • - Volume 2021
  • - Article ID 9969867
  • - Research Article

Exploring Security Vulnerabilities of Deep Learning Models by Adversarial Attacks

Xiaopeng Fu | Zhaoquan Gu | ... | Bin Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 4214784
  • - Research Article

An Effective Algorithm for Intrusion Detection Using Random Shapelet Forest

Gongliang Li | Mingyong Yin | ... | Bing Guo
  • Special Issue
  • - Volume 2021
  • - Article ID 5545648
  • - Research Article

A Privacy Protection Scheme for IoT Big Data Based on Time and Frequency Limitation

Lei Zhang | Yu Huo | ... | Wenlei Ouyang
  • Special Issue
  • - Volume 2021
  • - Article ID 5549109
  • - Research Article

A Model Study on Collaborative Learning and Exploration of RBAC Roles

Jiyong Yang | Xiajiong Shen | ... | HaoLin Chen
  • Special Issue
  • - Volume 2021
  • - Article ID 9935780
  • - Research Article

A Novel Privacy-Preserving Mobile-Coverage Scheme Based on Trustworthiness in HWSNs

Chunyang Qi | Jie Huang | ... | Hongkai Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 6648351
  • - Research Article

Network Intrusion Detection System Based on the Combination of Multiobjective Particle Swarm Algorithm-Based Feature Selection and Fast-Learning Network

Sajad Einy | Cemil Oz | Yahya Dorostkar Navaei
  • Special Issue
  • - Volume 2021
  • - Article ID 6627639
  • - Research Article

A New Approach Customizable Distributed Network Service Discovery System

Xiangzhan Yu | Zhichao Hu | Yi Xin
Wireless Communications and Mobile Computing
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Acceptance rate11%
Submission to final decision151 days
Acceptance to publication66 days
CiteScore2.300
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