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Electronics
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Data Security and Privacy Preserving in Data Society: Scenarios and...
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Data Security and Privacy Preserving in Data Society: Scenarios and Techniques

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

Deadline for manuscript submissions: 15 May 2024 | Viewed by 1237

Special Issue Editors

Prof. Dr. Licheng Wang

E-Mail Website
Guest Editor
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: cryptography; quantum computation
Prof. Dr. Xiaofang Huang

E-Mail Website
Guest Editor
School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 620010, China
Interests: data security
Dr. Daofeng Li

E-Mail Website
Guest Editor
School of Computer and Electronics Information, Guangxi University, Nanning 530004, China
Interests: public-key cryptography; web security

Special Issue Information

Dear Colleagues,

In our digital society, data play a dual role as a fundamental strategic resource and a critical production factor. On the one hand, valuable data resources are an important component of productivity and serve as the foundation for the development of numerous new industries, business models, and modes of the digital economy. On the other hand, the outstanding characteristic of data, in contrast to traditional production factors, is their multiplier effect on other resources, such as labor and capital, amplifying their value in the value chain of various industries. The purpose of this Special Issue is to analyze and explore the challenges of data security and privacy protection in various stages of the data life cycle, including data collection, storage, processing, circulation, analysis, and the ecosystem of digital society. Moreover, corresponding measures and technical systems will be designed for specific scenarios to safeguard the healthy development of digital society.

The topics of interest for this Special Issue include, but are not limited to, the following:

  • Comprehensive surveys on security and privacy issues in data society;
  • Privacy computing in data society;
  • Web security in data society;
  • Blockchain technology and applications in data society;
  • Zero trust and trust management solutions in data society;
  • Secure resource allocation and offload solutions in mobile edge computing;
  • Cryptographic schemes/protocols in data society;
  • Secure multi-party computing in data society;
  • AI security and applications in data society;
  • New designs and evaluations on post-quantum cryptography in data society.

Prof. Dr. Licheng Wang
Prof. Dr. Xiaofang Huang
Dr. Daofeng Li
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • comprehensive surveys on security and privacy issues in data society
  • privacy computing in data society
  • web security in data society
  • blockchain technology and applications in data society
  • zero trust and trust management solutions in data society
  • secure resource allocation and offload solutions in mobile edge computing
  • cryptographic schemes/protocols in data society
  • secure multi-party computing in data society
  • AI security and applications in data society
  • new designs and evaluations on post-quantum cryptography in data society

Published Papers (2 papers)

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Research

15 pages, 2310 KiB  
Article
AST-DF: A New Webshell Detection Method Based on Abstract Syntax Tree and Deep Forest
by Chengfeng Dong and Daofeng Li
Electronics 2024, 13(8), 1482; https://doi.org/10.3390/electronics13081482 - 13 Apr 2024
Viewed by 337
Abstract
Webshell is a kind of web-language-based website backdoor, which is usually used by attackers to control web servers. Due to its dangerous nature, how to detect Webshell effectively has become a hot research topic in current Web security research. With the rapid development [...] Read more.
Webshell is a kind of web-language-based website backdoor, which is usually used by attackers to control web servers. Due to its dangerous nature, how to detect Webshell effectively has become a hot research topic in current Web security research. With the rapid development of Webshell evasion technology, the existing Webshell detection methods have the problem of insufficient ability to detect unknown Webshells. In order to solve the above problems and achieve effective Webshell detection, this study proposes a Webshell detection method based on the abstract syntax tree (AST) and deep forest (DF) model called AST-DF. AST-DF first extracts the abstract syntax tree from the PHP code; then, the abstract syntax tree sequence is feature extracted and vectorized using N-gram and TF-IDF. Finally, the vectors are imported into the deep forest model for classification to determine whether the PHP code to be detected is a Webshell or not. The experimental results show that AST-DF achieves remarkable effects in the task of detecting PHP-type Webshells, with a 99.61% accuracy rate, and the values of precision, recall, and F1 score are more than 99%. Full article
(This article belongs to the Special Issue Data Security and Privacy Preserving in Data Society: Scenarios and Techniques)
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21 pages, 41539 KiB  
Article
Large-Scale Subspace Clustering Based on Purity Kernel Tensor Learning
by Yilu Zheng, Shuai Zhao, Xiaoqian Zhang, Yinlong Xu and Lifan Peng
Electronics 2024, 13(1), 83; https://doi.org/10.3390/electronics13010083 - 23 Dec 2023
Viewed by 564
Abstract
In conventional subspace clustering methods, affinity matrix learning and spectral clustering algorithms are widely used for clustering tasks. However, these steps face issues, including high time consumption and spatial complexity, making large-scale subspace clustering (LS2C) tasks challenging to execute effectively. To [...] Read more.
In conventional subspace clustering methods, affinity matrix learning and spectral clustering algorithms are widely used for clustering tasks. However, these steps face issues, including high time consumption and spatial complexity, making large-scale subspace clustering (LS2C) tasks challenging to execute effectively. To address these issues, we propose a large-scale subspace clustering method based on pure kernel tensor learning (PKTLS2C). Specifically, we design a pure kernel tensor learning (PKT) method to acquire as much data feature information as possible while ensuring model robustness. Next, we extract a small sample dataset from the original data and use PKT to learn its affinity matrix while simultaneously training a deep encoder. Finally, we apply the trained deep encoder to the original large-scale dataset to quickly obtain its projection sparse coding representation and perform clustering. Through extensive experiments on large-scale real datasets, we demonstrate that the PKTLS2C method outperforms existing LS2C methods in clustering performance. Full article
(This article belongs to the Special Issue Data Security and Privacy Preserving in Data Society: Scenarios and Techniques)
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