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Electronics
Special Issues
Data Privacy and Protection in IoT Systems
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Data Privacy and Protection in IoT Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 986

Special Issue Editors

Dr. Chengzu Dong

E-Mail Website
Guest Editor
School of Data Science, Lingnan University, Hong Kong 999077, China
Interests: UAV delivery; blockchain; person reid; IoT; edge computing; federated learning
Dr. Shao Di

E-Mail Website
Guest Editor
Pengcheng Laboratory, Shenzhen 518066, China
Interests: 3D point cloud analysis; unsupervised learning; computer vision
Dr. Aiting Yao

E-Mail Website
Guest Editor
Pengcheng Laboratory, Shenzhen 518066, China
Interests: resource allocation; task offloading; edge computing; decision-making; fuzzy sets; number; learning systems; Internet of Things; data privacy; blockchain; unmanned aerial vehicle; pilotless aircraft; network security
Dr. Xiangyu Song

E-Mail Website
Guest Editor
School of Data Science and Artificial Intelligence, Chang'an University, Xi'an 710064, China
Interests: graph structure data mining applications based on deep learning; a large language model for video of abnormal traffic events; multimodal learning applications (transportation, education)
Dr. Juan Zhao

E-Mail Website
Guest Editor
School of Data Science and Artificial Intelligence, Chang'an University, Xi'an 710064, China
Interests: artificial intelligence security; audio security; information hiding

Special Issue Information

Dear Colleagues,

Data privacy and protection in IoT systems address critical challenges arising from the proliferation of interconnected devices in smart cities, healthcare, industrial automation, and consumer applications. IoT devices, often deployed at the edge with limited security mechanisms, are threatened by unauthorized data access, device tampering, identity theft, and eavesdropping. Key solutions to these issues include lightweight encryption (e.g., homomorphic Paillier, SM9 signatures) for resource-constrained devices, blockchain-based authentication to ensure data integrity and decentralized trust, and privacy-preserving techniques such as hidden-policy access control and network coding, which anonymize sensitive information. Edge/fog computing architectures further mitigate latency and bandwidth issues while enhancing localized security. However, challenges related to scalability, energy efficiency, regulatory compliance (e.g., GDPR, CCPA), and resilience against evolving threats such as adversarial AI attacks persist. Future research should therefore address standardization, AI-driven threat detection, and cross-domain collaboration to balance innovation with robust privacy safeguards.

Dr. Chengzu Dong
Dr. Shao Di
Dr. Aiting Yao
Dr. Xiangyu Song
Dr. Juan Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • IoT data privacy
  • lightweight encryption
  • blockchain security
  • access control policies
  • edge computing security
  • anomaly detection
  • data integrity
  • privacy-preserving analytics
  • UAV system security
  • federated system security

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Published Papers (2 papers)

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Research

18 pages, 3705 KB  
Article
Cross-Platform Multi-Modal Transfer Learning Framework for Cyberbullying Detection
by Weiqi Zhang, Chengzu Dong, Aiting Yao, Asef Nazari and Anuroop Gaddam
Electronics 2026, 15(2), 442; https://doi.org/10.3390/electronics15020442 - 20 Jan 2026
Viewed by 157
Abstract
Cyberbullying and hate speech increasingly appear in multi-modal social media posts, where images and text are combined in diverse and fast changing ways across platforms. These posts differ in style, vocabulary and layout, and labeled data are sparse and noisy, which makes it [...] Read more.
Cyberbullying and hate speech increasingly appear in multi-modal social media posts, where images and text are combined in diverse and fast changing ways across platforms. These posts differ in style, vocabulary and layout, and labeled data are sparse and noisy, which makes it difficult to train detectors that are both reliable and deployable under tight computational budgets. Many high performing systems rely on large vision language backbones, full parameter fine tuning, online retrieval or model ensembles, which raises training and inference costs. We present a parameter efficient cross-platform multi-modal transfer learning framework for cyberbullying and hateful content detection. Our framework has three components. First, we perform domain adaptive pretraining of a compact ViLT backbone on in domain image-text corpora. Second, we apply parameter efficient fine tuning that updates only bias terms, a small subset of LayerNorm parameters and the classification head, leaving the inference computation graph unchanged. Third, we use noise aware knowledge distillation from a stronger teacher built from pretrained text and CLIP based image-text encoders, where only high confidence, temperature scaled predictions are used as soft labels during training, and teacher models and any retrieval components are used only offline. We evaluate primarily on Hateful Memes and use IMDB as an auxiliary text only benchmark to show that the deployment aware PEFT + offline-KD recipe can still be applied when other modalities are unavailable. On Hateful Memes, our student updates only 0.11% of parameters and retain about 96% of the AUROC of full fine-tuning. Full article
(This article belongs to the Special Issue Data Privacy and Protection in IoT Systems)
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16 pages, 5387 KB  
Article
Federated Distributed Network Traffic Classification Based on Deep Mutual Learning
by Hanxiao Xue, Yuyong Hu and Yu Wang
Electronics 2025, 14(24), 4928; https://doi.org/10.3390/electronics14244928 - 16 Dec 2025
Viewed by 408
Abstract
As encrypted traffic analysis becomes increasingly vital for network security, the conventional reliance on centralized classification faces growing challenges due to data privacy regulations and data silos across heterogeneous nodes. Federated learning (FL) emerges as a solution by training models locally and sharing [...] Read more.
As encrypted traffic analysis becomes increasingly vital for network security, the conventional reliance on centralized classification faces growing challenges due to data privacy regulations and data silos across heterogeneous nodes. Federated learning (FL) emerges as a solution by training models locally and sharing only parameter updates, thus preserving privacy. However, its performance is significantly degraded by data heterogeneity (i.e., non-IID data) among participants. To address this critical challenge, this paper proposes a Federated Learning framework based on Deep Mutual Learning (FLDML). In this method, clients first train local models on their private traffic data and then upload them to a server. There, they engage in deep mutual learning through co-training on a shared public dataset to enhance robustness and mitigate data heterogeneity. Subsequently, a global classifier is generated by averaging the model parameters. When evaluated on the ISCX VPN-NonVPN 2016 dataset, FLDML demonstrates significantly superior performance in handling non-IID traffic data compared to classical FL algorithms. This study concludes that the proposed framework not only effectively mitigates data heterogeneity in federated scenarios but also provides a scalable and improved solution for distributed network traffic classification. Full article
(This article belongs to the Special Issue Data Privacy and Protection in IoT Systems)
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