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Electronics
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Advances in Data Security: Challenges, Technologies, and Applications
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Advances in Data Security: Challenges, Technologies, and Applications

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 2917

Special Issue Editors

Dr. Lanting Fang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: data mining; natural language processing; graph neural network
Special Issues, Collections and Topics in MDPI journals
Dr. Yubo Song

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
Interests: data mining; data privacy; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's data-driven world, the exponential growth of digital information and the rapid adoption of emerging technologies such as cloud computing, the Internet of Things (IoT), and artificial intelligence have brought unprecedented opportunities—and significant security challenges. Ensuring data security has become a critical concern for researchers, practitioners, and policymakers across a wide range of domains, from finance and healthcare to government and industrial systems.

This Special Issue aims to provide a comprehensive forum for the latest research and developments in data security. We invite original contributions that address fundamental challenges, propose innovative technologies, or demonstrate practical applications for securing data across diverse platforms and environments. Topics of interest include, but are not limited to, the following:

  • Data encryption and secure computation;
  • Privacy-preserving machine learning;
  • Blockchain and distributed ledger technologies for data security;
  • Intrusion detection and anomaly detection in large-scale systems;
  • Secure data sharing and access control;
  • Security in edge and cloud computing environments;
  • Cyber–physical systems and IoT data protection;
  • Formal methods and verification for data security;
  • Adversarial attacks and defenses in data-driven models;
  • Real-world case studies and applications in critical sectors.

By bringing together interdisciplinary perspectives, this Special Issue seeks to foster a deeper understanding of data security challenges and promote the development of robust, scalable, and practical security solutions for the future.

Dr. Lanting Fang
Dr. Yubo Song
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

  • data mining
  • data encryption
  • machine learning
  • data security

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Published Papers (3 papers)

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Research

11 pages, 684 KB  
Article
Replacing Batch Normalization with Memory-Based Affine Transformation for Test-Time Adaptation
by Jih Pin Yeh, Joe-Mei Feng, Hwei Jen Lin and Yoshimasa Tokuyama
Electronics 2025, 14(21), 4251; https://doi.org/10.3390/electronics14214251 - 30 Oct 2025
Viewed by 549
Abstract
Batch normalization (BN) has become a foundational component in modern deep neural networks. However, one of its disadvantages is its reliance on batch statistics that may be unreliable or unavailable during inference, particularly under test-time domain shifts. While batch-statistics-free affine transformation methods alleviate [...] Read more.
Batch normalization (BN) has become a foundational component in modern deep neural networks. However, one of its disadvantages is its reliance on batch statistics that may be unreliable or unavailable during inference, particularly under test-time domain shifts. While batch-statistics-free affine transformation methods alleviate this by learning per-sample scale and shift parameters, most treat samples independently, overlooking temporal or sequential correlations in streaming or episodic test-time settings. We propose LSTM-Affine, a memory-based normalization module that replaces BN with a recurrent parameter generator. By leveraging an LSTM, the module produces channel-wise affine parameters conditioned on both the current input and its historical context, enabling gradual adaptation to evolving feature distributions. Unlike conventional batch-statistics-free designs, LSTM-Affine captures dependencies across consecutive samples, improving stability and convergence in scenarios with gradual distribution shifts. Extensive experiments on few-shot learning and source-free domain adaptation benchmarks demonstrate that LSTM-Affine consistently outperforms BN and prior batch-statistics-free baselines, particularly when adaptation data are scarce or non-stationary. Full article
(This article belongs to the Special Issue Advances in Data Security: Challenges, Technologies, and Applications)
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23 pages, 1659 KB  
Article
A Multi-View-Based Federated Learning Approach for Intrusion Detection
by Jia Yu, Guoqiang Wang, Nianfeng Shi, Raghav Saxena and Brian Lee
Electronics 2025, 14(21), 4166; https://doi.org/10.3390/electronics14214166 - 24 Oct 2025
Viewed by 760
Abstract
Intrusion detection aims to identify the unauthorized activities within computer networks or systems by classifying events into normal or abnormal categories. As modern scenarios often involve multi-source data, multi-view fusion deep learning methods are employed to leverage diverse viewpoints for enhancing security threat [...] Read more.
Intrusion detection aims to identify the unauthorized activities within computer networks or systems by classifying events into normal or abnormal categories. As modern scenarios often involve multi-source data, multi-view fusion deep learning methods are employed to leverage diverse viewpoints for enhancing security threat detection. This paper introduces a novel intrusion detection approach using multi-view fusion within a federated learning framework, proposing an integrated AE Neural SVM (AE-NSVM) model that combines auto-encoder (AE) multi-view feature extraction and Support Vector Machine (SVM) classification. This approach simultaneously learns representative features from multiple views and classifies network samples into normal or seven attack categories while employing federated learning across clients to ensure adaptability and robustness in diverse network environments. The experimental results obtained from two benchmark datasets validate its superiority: on TON_IoT, the CAE-NSVM model achieves a highest F1-measure of 0.792 (1.4% higher than traditional pipeline systems); on UNSW-NB15, it delivers an F1-score of 0.829 with a 73% reduced training time and an 89% faster inference compared to baseline models. These results demonstrate the advantages of multi-view fusion in federated learning for balancing accuracy and efficiency in distributed intrusion detection systems. Full article
(This article belongs to the Special Issue Advances in Data Security: Challenges, Technologies, and Applications)
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20 pages, 914 KB  
Article
LR-SQL: A Supervised Fine-Tuning Method for Text2SQL Tasks Under Low-Resource Scenarios
by Wuzhenghong Wen, Yongpan Zhang, Su Pan, Yuwei Sun, Pengwei Lu and Cheng Ding
Electronics 2025, 14(17), 3489; https://doi.org/10.3390/electronics14173489 - 31 Aug 2025
Viewed by 1384
Abstract
In supervised fine-tuning (SFT) for Text2SQL tasks, particularly for databases with numerous tables, encoding schema features requires excessive tokens, escalating GPU resource requirements during fine-tuning. To bridge this gap, we propose LR-SQL, a general dual-model SFT framework comprising a schema linking model and [...] Read more.
In supervised fine-tuning (SFT) for Text2SQL tasks, particularly for databases with numerous tables, encoding schema features requires excessive tokens, escalating GPU resource requirements during fine-tuning. To bridge this gap, we propose LR-SQL, a general dual-model SFT framework comprising a schema linking model and an SQL generation model. At the core of our framework lies the schema linking model, which is trained on a novel downstream task termed slice-based related table filtering. This task dynamically partitions a database into adjustable slices of tables and sequentially evaluates the relevance of each slice to the input query, thereby reducing token consumption per iteration. However, slicing fragments destroys database information, impairing the model’s ability to comprehend the complete database. Thus, we integrate Chain of Thought (CoT) in training, enabling the model to reconstruct the full database context from discrete slices, thereby enhancing inference fidelity. Ultimately, the SQL generation model uses the result from the schema linking model to generate the final SQL. Extensive experiments demonstrate that our proposed LR-SQL reduces total GPU memory usage by 40% compared to baseline SFT methods, with only a 2% drop in table prediction accuracy for the schema linking task and a negligible 0.6% decrease in overall Text2SQL Execution Accuracy. Full article
(This article belongs to the Special Issue Advances in Data Security: Challenges, Technologies, and Applications)
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