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Electronics
Special Issues
Data Security and Privacy: Challenges and Techniques
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Data Security and Privacy: Challenges and Techniques

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 2113

Special Issue Editors

Dr. Jianyi Liu

E-Mail Website
Guest Editor
School of Cyberspace Security, Bei**g University of Posts and Telecommunications, Bei**g 100876, China
Interests: network security; intelligent information processing; data mining
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ru Zhang

E-Mail Website
Guest Editor
School of Cyberspace Security, Bei**g University of Posts and Telecommunications, Bei**g 100876, China
Interests: network security; intelligent information processing; data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Zhen Yang

E-Mail Website
Guest Editor
School of Cyberspace Security, Bei**g University of Posts and Telecommunications, Bei**g 100876, China
Interests: big data security; privacy protection; digital content security

Special Issue Information

Dear Colleagues,

With an increasing amount of data, people have become more aware of the risks and impacts of data security and privacy protection, which has led to new challenges for information systems and service providers. Data security and privacy are essential to ensuring accountability and responsibility. In recent years, multiple techniques which enhance data security and privacy preservation capabilities have flourished. Some noticeable terms include federated learning, adversarial learning, privacy computing, steganography, zero-trust models, and AI security, among others. These techniques have both broadened the scope of data security and also strengthened the level of privacy preservation. This Special Issue provides an opportunity to present the latest developments in the domains of data security and privacy enhancing.

This Special Issue seeks novel theoretical or applied research on designing, develo**, integrating, testing, and evaluating approaches for systems or approaches in protecting data security and privacy. Studies relating to data and privacy protection principles, regulations, or techniques, including new theories, foundations, tools, and significant case studies of implementations, are welcome. This Special Issue also welcomes survey papers that give the reader an overview of the state of the art in these topics.

Potential topics include, but are not limited to, the following:

  1. Data security and privacy in deep learning;
  2. Privacy-enhancing technologies;
  3. Secure federated machine learning;
  4. Digital watermarking for the protection of multimedia, AI, and language models;
  5. Adversarial attack in specific applications;
  6. Anonymous communication and anonymous communication networks;
  7. Steganography in emerging scenarios;
  8. Forensics and detection;
  9. Research challenges in covert channels;
  10. Novel ideas, algorithms, models, frameworks, and systems for data security and privacy.

Dr. Jianyi Liu
Prof. Dr. Ru Zhang
Dr. Zhen Yang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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

  • data security
  • privacy
  • digital watermarking
  • steganography
  • forensics and detection

Published Papers (3 papers)

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Research

15 pages, 736 KiB  
Article
A Security Analysis of Cryptocurrency Wallets against Password Brute-Force Attacks
by Hyeonsu Byun, Jueun Kim, Yunseok Jeong, Byoung** Seok, Seonghyeon Gong and Changhoon Lee
Electronics 2024, 13(13), 2433; https://doi.org/10.3390/electronics13132433 - 21 Jun 2024
Viewed by 351
Abstract
Currently, the monetary value of cryptocurrencies is extremely high, leading to frequent theft attempts. Cyberattacks targeting cryptocurrency wallets and the scale of these attacks are also increasing annually. However, many studies focus on large-scale exchanges, leading to a lack of research on cryptocurrency [...] Read more.
Currently, the monetary value of cryptocurrencies is extremely high, leading to frequent theft attempts. Cyberattacks targeting cryptocurrency wallets and the scale of these attacks are also increasing annually. However, many studies focus on large-scale exchanges, leading to a lack of research on cryptocurrency wallet security. Nevertheless, the threat to individual wallets is real and can lead to severe consequences for individuals. In this paper, we analyze the security of the open-source cryptocurrency wallets Sparrow, Etherwall, and Bither against brute-force attacks, a fundamental threat in password-based systems. As cryptocurrency wallets use passwords to manage users’ private keys, we analyzed the private key management mechanism and implemented a password verification oracle. We used this oracle for brute-force attacks. We identified the private key management mechanism by conducting a code-level investigation and evaluated the three wallets’ security through practical experimentation. The experiment results revealed that the wallets’ security, which depends on passwords, could be diminished due to the password input space and the configuration of password length settings. We propose a general methodology for analyzing the security of desktop cryptocurrency wallets against brute-force attacks and provide practical guidelines for designing secure wallets. By using the analysis methods suggested in this paper, one can evaluate the security of wallets. Full article
(This article belongs to the Special Issue Data Security and Privacy: Challenges and Techniques)
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23 pages, 2784 KiB  
Article
Enhancing Steganography through Optimized Quantization Tables
by Rasa Brūzgienė, Algimantas Venčkauskas, Šarūnas Grigaliūnas and Jonas Petraška
Electronics 2024, 13(12), 2415; https://doi.org/10.3390/electronics13122415 - 20 Jun 2024
Viewed by 376
Abstract
This paper addresses the scientific problem of enhancing the security and capacity of steganographic methods for protecting digital media. The primary aim is to develop an advanced steganographic technique that optimizes quantization tables to surpass the traditional F5 algorithm in terms of security, [...] Read more.
This paper addresses the scientific problem of enhancing the security and capacity of steganographic methods for protecting digital media. The primary aim is to develop an advanced steganographic technique that optimizes quantization tables to surpass the traditional F5 algorithm in terms of security, capacity, and robustness. The novelty of this research lies in the introduction of the F5A method, which utilizes optimized quantization tables to significantly increase the capacity for concealed information while ensuring high-quality image retention and resistance to unauthorized content recovery. The F5A method integrates cryptographic keys and features to detect and prevent copyright infringement in real time. Experimental evaluations demonstrate that the F5A method improves the mean square error and peak signal-to-noise ratio indices by 1.716 and 1.121 times, respectively, compared to the traditional F5 algorithm. Additionally, it increases the steganographic capacity by up to 1.693 times for smaller images and 1.539 times for larger images. These results underscore the effectiveness of the F5A method in enhancing digital media security and copyright protection. Full article
(This article belongs to the Special Issue Data Security and Privacy: Challenges and Techniques)
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21 pages, 7974 KiB  
Article
ProvGRP: A Context-Aware Provenance Graph Reduction and Partition Approach for Facilitating Attack Investigation
by Jiawei Li, Ru Zhang and Jianyi Liu
Electronics 2024, 13(1), 100; https://doi.org/10.3390/electronics13010100 - 25 Dec 2023
Viewed by 954
Abstract
Attack investigation is a crucial technique in proactively defending against sophisticated attacks. Its purpose is to identify attack entry points and previously unknown attack traces through comprehensive analysis of audit data. However, a major challenge arises from the vast and redundant nature of [...] Read more.
Attack investigation is a crucial technique in proactively defending against sophisticated attacks. Its purpose is to identify attack entry points and previously unknown attack traces through comprehensive analysis of audit data. However, a major challenge arises from the vast and redundant nature of audit logs, making attack investigation difficult and prohibitively expensive. To address this challenge, various technologies have been proposed to reduce audit data, facilitating efficient analysis. However, most of these techniques rely on defined templates without considering the rich context information of events. Moreover, these methods fail to remove false dependencies caused by the coarse-grained nature of logs. To address these limitations, this paper proposes a context-aware provenance graph reduction and partition approach for facilitating attack investigation named ProvGRP. Specifically, three features are proposed to determine whether system events are the same behavior from multiple dimensions. Based on the insight that information paths belonging to the same high-level behavior share similar information flow patterns, ProvGRP generates information paths containing context, and identifies and merges paths that share similar flow patterns. Experimental results show that ProvGRP can efficiently reduce provenance graphs with minimal loss of crucial information, thereby facilitating attack investigation in terms of runtime and results. Full article
(This article belongs to the Special Issue Data Security and Privacy: Challenges and Techniques)
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