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Applied Sciences
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Big Data Security and Privacy in Internet of Things
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Big Data Security and Privacy in Internet of Things

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 8236

Special Issue Editors

Dr. Xiaobo Zhou

E-Mail Website
Guest Editor
School of Computer Science and Technology, Tianjin University, Tianjin, China
Interests: cloud/edge computing; mobile computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Dong Yuan

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, The University of Sydney, Darlington, NSW, Australia
Interests: cloud and edge computing; AI, deep learning; Internet of Things; workflow
Dr. Lei Yang

E-Mail Website
Guest Editor
School of Economics and Commerce, South China University of Technology, Guangzhou, China
Interests: mobile cloud computing; edge computing; Internet of Things

Special Issue Information

Dear Colleagues,

With the rapid development of the Internet of Things (IoT), a tremendous number of data have been captured by pervasive sensors and transmitted to the central cloud platform for storage or further analytics. To satisfy users’ expectation for improved quality of service (QoS) and/or quality of experience (QoE) requirements in various application scenarios, such as disaster monitoring, healthcare, smart cities and self-driving, numerous new frameworks and techniques have emerged to facilitate data acquisition, transmission, storage and analysis. Such methods include edge/fog computing, the blockchain, federated learning and other distributed machine learning techniques. Due to big data’s typical characteristics, namely velocity, volume, variety and value, traditional security and privacy mechanisms are inadequate and unable to cope with the rapid explosion of data in this complex distributed computing environment. This Special Issue is dedicated to presenting advances in big data security and privacy issues and challenges in IoT. We will cover security mitigation and privacy protection frameworks, mechanisms and techniques involved in data acquisition, transmission, storage and analysis. Papers detailing the most significant challenges and trends in the abovementioned research are welcome.

Dr. Xiaobo Zhou
Dr. Dong Yuan
Dr. Lei Yang
Guest Editors

Manuscript Submission Information

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Keywords

  • big data security
  • big data privacy
  • Internet of Things
  • edge/fog computing
  • edge caching
  • blockchain
  • federated learning
  • distributed machine learning

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Published Papers (3 papers)

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Research

19 pages, 4955 KiB  
Article
Qualitative and Quantitative Evaluation of Multivariate Time-Series Synthetic Data Generated Using MTS-TGAN: A Novel Approach
by Parul Yadav, Manish Gaur, Nishat Fatima and Saqib Sarwar
Appl. Sci. 2023, 13(7), 4136; https://doi.org/10.3390/app13074136 - 24 Mar 2023
Cited by 10 | Viewed by 3140
Abstract
To obtain high performance, generalization, and accuracy in machine learning applications, such as prediction or anomaly detection, large datasets are a necessary prerequisite. Moreover, the collection of data is time-consuming, difficult, and expensive for many imbalanced or small datasets. These challenges are evident [...] Read more.
To obtain high performance, generalization, and accuracy in machine learning applications, such as prediction or anomaly detection, large datasets are a necessary prerequisite. Moreover, the collection of data is time-consuming, difficult, and expensive for many imbalanced or small datasets. These challenges are evident in collecting data for financial and banking services, pharmaceuticals and healthcare, manufacturing and the automobile, robotics car, sensor time-series data, and many more. To overcome the challenges of data collection, researchers in many domains are becoming more and more interested in the development or generation of synthetic data. Generating synthetic time-series data is far more complicated and expensive than generating synthetic tabular data. The primary objective of the paper is to generate multivariate time-series data (for continuous and mixed parameters) that are comparable and evaluated with real multivariate time-series synthetic data. After being trained to produce such data, a novel GAN architecture named as MTS-TGAN is proposed and then assessed using both qualitative measures namely t-SNE, PCA, discriminative and predictive scores as well as quantitative measures, for which an RNN model is implemented, which calculates MAE and MSLE scores for three training phases; Train Real Test Real, Train Real Test Synthetic and Train Synthetic Test Real. The model is able to reduce the overall error up to 13% and 10% in predictive and discriminative scores, respectively. The research’s objectives are met, and the outcomes demonstrate that MTS-TGAN is able to pick up on the distribution and underlying knowledge included in the attributes of the real data and it can serve as a starting point for additional research in the respective area. Full article
(This article belongs to the Special Issue Big Data Security and Privacy in Internet of Things)
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18 pages, 438 KiB  
Article
Distributed Online Multi-Label Learning with Privacy Protection in Internet of Things
by Fan Huang , Nan Yang, Huaming Chen , Wei Bao and Dong Yuan
Appl. Sci. 2023, 13(4), 2713; https://doi.org/10.3390/app13042713 - 20 Feb 2023
Viewed by 1592
Abstract
With the widespread use of end devices, online multi-label learning has become popular as the data generated by users using the Internet of Things devices have become huge and rapidly updated. However, in many scenarios, the user data are often generated in a [...] Read more.
With the widespread use of end devices, online multi-label learning has become popular as the data generated by users using the Internet of Things devices have become huge and rapidly updated. However, in many scenarios, the user data are often generated in a geographically distributed manner that is often inefficient and difficult to centralize for training machine learning models. At the same time, current mainstream distributed learning algorithms always require a centralized server to aggregate data from distributed nodes, which inevitably causes risks to the privacy of users. To overcome this issue, we propose a distributed approach for multi-label classification, which trains the models in distributed computing nodes without sharing the source data from each node. In our proposed method, each node trains its model with its local online data while it also learns from the neighbour nodes without transferring the training data. As a result, our proposed method achieved the online distributed approach for multi-label classification without losing performance when taking existing centralized algorithms as a reference. Experiments show that our algorithm outperforms the centralized online multi-label classification algorithm in F1 score, being 0.0776 higher in macro F1 score and 0.1471 higher for micro F1 score on average. However, for the Hamming loss, both algorithms beat each other on some datasets, and our proposed algorithm loses 0.005 compared to the centralized approach on average, which can be neglected. Furthermore, the size of the network and the degree of connectivity are not factors that affect the performance of this distributed online multi-label learning algorithm. Full article
(This article belongs to the Special Issue Big Data Security and Privacy in Internet of Things)
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13 pages, 1636 KiB  
Article
FocalMatch: Mitigating Class Imbalance of Pseudo Labels in Semi-Supervised Learning
by Yongkun Deng, Chenghao Zhang, Nan Yang and Huaming Chen
Appl. Sci. 2022, 12(20), 10623; https://doi.org/10.3390/app122010623 - 20 Oct 2022
Cited by 1 | Viewed by 2134
Abstract
Semi-supervised learning (SSL) is a popular research area in machine learning which utilizes both labeled and unlabeled data. As an important method for the generation of artificial hard labels for unlabeled data, the pseudo-labeling method is introduced by applying a high and fixed [...] Read more.
Semi-supervised learning (SSL) is a popular research area in machine learning which utilizes both labeled and unlabeled data. As an important method for the generation of artificial hard labels for unlabeled data, the pseudo-labeling method is introduced by applying a high and fixed threshold in most state-of-the-art SSL models. However, early models prefer certain classes that are easy to learn, which results in a high-skewed class imbalance in the generated hard labels. The class imbalance will lead to less effective learning of other minority classes and slower convergence for the training model. The aim of this paper is to mitigate the performance degradation caused by class imbalance and gradually reduce the class imbalance in the unsupervised part. To achieve this objective, we propose FocalMatch, a novel SSL method that combines FixMatch and focal loss. Our contribution of FocalMatch adjusts the loss weight of various data depending on how well their predictions match up with their pseudo labels, which can accelerate system learning and model convergence and achieve state-of-the-art performance on several semi-supervised learning benchmarks. Particularly, its effectiveness is demonstrated with the dataset that has extremely limited labeled data. Full article
(This article belongs to the Special Issue Big Data Security and Privacy in Internet of Things)
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