Security and Communication Networks

Security and Privacy Challenges in Internet of Things and Mobile Edge Computing


Publishing date
01 Sep 2021
Status
Closed
Submission deadline
07 May 2021

Lead Editor

1Fujian Normal University, Fuzhou, China

2University of North Texas, Denton, USA

3Sam Houston State University, Houston, USA

4Nanyang Technological University, Singapore

This issue is now closed for submissions.
More articles will be published in the near future.

Security and Privacy Challenges in Internet of Things and Mobile Edge Computing

This issue is now closed for submissions.
More articles will be published in the near future.

Description

In the era of Internet of Things (IoT), a large number of physical objects embedded with sensors and electronic devices exchange information through heterogeneous networks in various applications such as smart cities, electronic medical treatment and autonomous driving. The perception paradigm of Internet of everything has greatly changed our life bringing opportunities and challenges. Hundreds of millions of sensory devices continuously generate contextual data and exchange real-time analysis and decisions. Traditional cloud computing based on centralised storage is incapable of meeting the requirements of data-intensive services. Mobile edge computing (MEC) has been accepted as an effective solution to support data-driven services and local IoT applications, offloading computing, storage, and network resources from the cloud to the edge of the network.

Despite the advantages of MEC, such as high bandwidth, low latency, high efficiency, and scalability, it faces severe challenges in data security and privacy protection. IoT devices upload data to edge nodes in exchange for services, such as training models. Direct data uploads may jeopardize users' privacy, and malicious data tampering can mislead service decisions and even endanger the security of public services. Therefore, before upload, data needs to be protected using reliable security measures, such as homomorphic encryption, secure multiparty computing, differential privacy, and other encryption tools. Neural network is a widely used intelligent analysis tool. How to accurately and efficiently realise neural network reasoning and training over ciphertext is a critical challenge.

The aim of this Special Issue is to solicit original research articles highlighting the recent developments on security and privacy solutions for IoT and MEC. Review articles discussing the state of the art are also welcome.

Potential topics include but are not limited to the following:

  • Efficient data encryption scheme for MEC
  • Secure heterogeneous data aggregation and fusion scheme for IoT and MEC
  • Secure data deduplication scheme for IoT and MEC
  • Practical application of generating counter samples for IoT and MEC
  • Secure data sharing and forwarding framework for IoT devices
  • Secure and efficient client-to-edge collaborative computing for IoT and MEC
  • Lightweight secure computing protocol design and formalise security analysis
  • Privacy-preserving agent-based neural network reasoning of IoT and MEC
  • Privacy-preserving outsourced neural network training of IoT and MEC
  • Secure, verifiable, personalized service scheme for IoT and MEC
  • New comprehensive evaluation system for IoT and MEC
  • Programmable security framework for privacy-preserving computing
  • Reasonable communication and computing resource allocation strategies
  • Attack and defense in neural network for IoT and MEC
  • Future privacy challenges and solutions in IoT and MEC

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 8609278
  • - Research Article

GNS: Forge High Anonymity Graph by Nonlinear Scaling Spectrum

Yong Zeng | Yixin Li | ... | Jianfeng Ma
  • Special Issue
  • - Volume 2021
  • - Article ID 7776193
  • - Research Article

Edge Computing Assisted an Efficient Privacy Protection Layered Data Aggregation Scheme for IIoT

Rong Ma | Tao Feng | Junli Fang
  • Special Issue
  • - Volume 2021
  • - Article ID 7354316
  • - Research Article

Private Data Aggregation Based on Fog-Assisted Authentication for Mobile Crowd Sensing

Ruyan Wang | Shiqi Zhang | ... | Alexander Fedotov
  • Special Issue
  • - Volume 2021
  • - Article ID 5448397
  • - Research Article

TASC-MADM: Task Assignment in Spatial Crowdsourcing Based on Multiattribute Decision-Making

Yunhui Li | Liang Chang | ... | Tianlong Gu
  • Special Issue
  • - Volume 2021
  • - Article ID 4804758
  • - Research Article

Privacy-Preserving Incentive Mechanism for Mobile Crowdsensing

Tao Wan | Shixin Yue | Weichuan Liao
  • Special Issue
  • - Volume 2021
  • - Article ID 2037188
  • - Research Article

An Efficient and Provable Multifactor Mutual Authentication Protocol for Multigateway Wireless Sensor Networks

Shuailiang Zhang | Xiujuan Du | Xin Liu
  • Special Issue
  • - Volume 2021
  • - Article ID 9179286
  • - Research Article

From Unknown to Similar: Unknown Protocol Syntax Analysis for Network Flows in IoT

Yichuan Wang | Han Yu | ... | Wenjiang Ji
  • Special Issue
  • - Volume 2021
  • - Article ID 1805615
  • - Research Article

Improved Outsourced Provable Data Possession for Secure Cloud Storage

Haibin Yang | Zhengge Yi | ... | Xiaoyuan Yang
  • Special Issue
  • - Volume 2021
  • - Article ID 9717747
  • - Research Article

Anticollusion Attack Strategy Combining Trust Metrics and Secret Sharing for Friendships Protection

Junfeng Tian | Yue Li
  • Special Issue
  • - Volume 2021
  • - Article ID 2296386
  • - Research Article

A Secure Truth Discovery for Data Aggregation in Mobile Crowd Sensing

Taochun Wang | Chengmei Lv | ... | Yonglong Luo
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