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JCP
Special Issues
Data Protection and Privacy
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Data Protection and Privacy

A special issue of Journal of Cybersecurity and Privacy (ISSN 2624-800X). This special issue belongs to the section "Privacy".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 21631

Special Issue Editors

Prof. Dr. Chrysostomos Stylios

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece
Interests: security; privacy; VR; AI; data structures; machine learning; industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Vasiliki Liagkou

E-Mail Website
Guest Editor
Department of Informatics & Telecommunications, University of Ioannina, 45110 Ioannina, Greece
Interests: system cryptanalysis; system security; trust management; pseudorandom generators; algorithm engineering; number theory; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Dr. Kyriakos Stefanidis

E-Mail Website
Guest Editor
Industrial Systems Institute, 26504 Athena, Greece
Interests: cybersecurity; incident response; data security; intrusion detection and malware analysis social media account
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 includes many technological aspects that have led to an integrated digital manufacturing environment. The thoroughly interconnected ecosystem of Industry 4.0 has to meet many security challenges and threats for each component. Preserving security plays a crucial role in Industry 4.0, and it is vital for its existence; the key issue is how to ensure the confidentiality, integrity, and availability of the information shared among the Industry 4.0 components.

In addition to this, the significant and rapid inclusion of the Internet of Things (IoT) in our daily lives, together with the rapidly increasing number of cyber security incidents, further stress the need to strengthen cyber resilience and preserve users’ privacy when it comes to exposure in the IoT environment. The large attack surface in terms of connected devices and the complex processes involved in the IoT ecosystem can lead to more sophisticated physical attacks on IoT systems.

With such a wide attack surface, these innovative and emerging infrastructures and applications based on IoT can effectively serve their purpose only if privacy and security challenges are addressed.

This Special Issue aims to solicit high-quality research articles addressing key challenges and state-of-the-art solutions for security and privacy issues related to Industry 4.0 technologies and applications.

Prof. Dr. Chrysostomos Stylios
Dr. Vasiliki Liagkou
Dr. Kyriakos Stefanidis
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. Journal of Cybersecurity and Privacy is an international peer-reviewed open access quarterly 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 1000 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

  • cybersecurity and privacy in industrial environments
  • security in cyber–physical environments
  • cryptography in I4.0
  • security and privacy in industrial control systems
  • IoT security and privacy
  • IoT system and network security
  • privacy protection and privacy-by-design
  • blockchains and smart contracts for IoT
  • trust issues in intelligent IoT devices
  • IoT threat detection and risk management
  • incident response and vulnerability management in IoT infrastructures
  • IoT privacy protection
  • secure data management and trading in industrial environments
  • privacy-enhancing technologies for ΙοΤ devices
  • IoT Identity management
  • artificial intelligence (AI)-based security
  • machine learning and data protection for I4.0
  • standardization activities for I4.0 security
  • quantum and post-quantum I4.0 cryptography
  • IoT side-channel attacks

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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31 pages, 1674 KiB  
Article
Protection of Personal Data in the Context of E-Commerce
by Zlatan Morić, Vedran Dakic, Daniela Djekic and Damir Regvart
J. Cybersecur. Priv. 2024, 4(3), 731-761; https://doi.org/10.3390/jcp4030034 - 20 Sep 2024
Cited by 2 | Viewed by 9451
Abstract
This paper examines the impact of stringent regulations on personal data protection on customer perception of data security and online shopping behavior. In the context of the rapidly expanding e-commerce landscape, ensuring the security of personal data is a complex and crucial task. [...] Read more.
This paper examines the impact of stringent regulations on personal data protection on customer perception of data security and online shopping behavior. In the context of the rapidly expanding e-commerce landscape, ensuring the security of personal data is a complex and crucial task. The study of several legal frameworks, including Malaysia’s compliance with EU regulations and Indonesia’s Personal Data Protection Law, provides valuable insights into consumer data protection. The challenges of balancing data safeguarding and unrestricted movement and tackling misuse by external entities are significant and require careful consideration. This research elucidates the pivotal role of trust in e-commerce environments and the deployment of innovative e-commerce models designed to minimize personal data sharing. By integrating advanced privacy-enhancing technologies and adhering to stringent regulatory standards such as the GDPR, this study demonstrates effective strategies for robust data protection. The paper contributes to the academic discourse by providing a comprehensive framework that synergizes legal, technological, and procedural elements to fortify data security and enhance consumer trust in digital marketplaces. This approach aligns with international data protection standards and offers a pragmatic blueprint for achieving sustainable data security in e-commerce. Full article
(This article belongs to the Special Issue Data Protection and Privacy)
►▼ Show Figures

Figure 1

Figure 1
<p>An online webshop data workflow example.</p> Full article ">Figure 2
<p>Many conveniences of online shopping for customers.</p> Full article ">Figure 3
<p>Structure of respondents by gender and age.</p> Full article ">Figure 4
<p>Frequency of online purchases.</p> Full article ">Figure 5
<p>Concerns regarding online shopping.</p> Full article ">
17 pages, 1177 KiB  
Article
Mapping Automated Cyber Attack Intelligence to Context-Based Impact on System-Level Goals
by Pete Burnap, Eirini Anthi, Philipp Reineckea, Lowri Williams, Fengnian Cao, Rakan Aldmoura and Kevin Jones
J. Cybersecur. Priv. 2024, 4(2), 340-356; https://doi.org/10.3390/jcp4020017 - 6 Jun 2024
Viewed by 1763
Abstract
Traditionally, cyber risk assessment considers system-level risk separately from individual component-level risk, i.e., devices, data, people. This separation prevents effective impact assessment where attack intelligence for a specific device can be mapped to its impact on the entire system, leading to cascading failures. [...] Read more.
Traditionally, cyber risk assessment considers system-level risk separately from individual component-level risk, i.e., devices, data, people. This separation prevents effective impact assessment where attack intelligence for a specific device can be mapped to its impact on the entire system, leading to cascading failures. Furthermore, risk assessments typically follow a failure or attack perspective, focusing on potential problems, which means they need to be updated as attacks evolve. This approach does not scale to modern digital ecosystems. In this paper, we present a Data Science approach, which involves using machine learning algorithms and statistical models to analyse and predict the impact of cyber attacks. Specifically, this approach integrates automated attack detection on specific devices with a systems view of risk. By mapping operational goals in a top-down manner, we transform attack intelligence on individual components into system success probabilities. Full article
(This article belongs to the Special Issue Data Protection and Privacy)
►▼ Show Figures

Figure 1

Figure 1
<p>Exponential correlation by different Damage damage potential—not to zero (low-damage attacks (top line (green)) to high-damage attacks (bottom line (blue)).</p> Full article ">Figure 2
<p>Exponential correlation by different damage potential—to zero (low-damage attacks (top line (green)) to high-damage attacks (bottom line (blue)).</p> Full article ">Figure 3
<p>Logarithmic correlation by different damage potential—not to zero (low-damage attacks (bottom line (green)) to high-damage attacks (top line (blue)).</p> Full article ">Figure 4
<p>Logarithmic correlation by different damage potential—to zero (low-damage attacks (bottom line (green)) to high-damage attacks (top line (blue)).</p> Full article ">Figure 5
<p>Perfect negative correlation by different damage potential (gradients range from no effect (top line (red)) to a complete failure effect (bottom diagonal line (blue)).</p> Full article ">Figure 6
<p>Procedure of mapping attacks to distributions.</p> Full article ">Figure 7
<p>Denial of Service attack (DoS).</p> Full article ">Figure 8
<p>Distribution of Remote Control.</p> Full article ">Figure 9
<p>MITM attack.</p> Full article ">Figure 10
<p>Distribution of physical damage.</p> Full article ">Figure 11
<p>Dependency model pre-attack (red = probability of a cyber attack occurring, green = probability of a healthy system).</p> Full article ">Figure 12
<p>Dependency model after attack (red = probability of a cyber attack occurring, green = probability of a healthy system).</p> Full article ">
21 pages, 1824 KiB  
Article
Incidental Data: A Survey towards Awareness on Privacy-Compromising Data Incidentally Shared on Social Media
by Stefan Kutschera, Wolfgang Slany, Patrick Ratschiller, Sarina Gursch, Patrick Deininger and Håvard Dagenborg
J. Cybersecur. Priv. 2024, 4(1), 105-125; https://doi.org/10.3390/jcp4010006 - 23 Feb 2024
Cited by 1 | Viewed by 3172
Abstract
Sharing information with the public is becoming easier than ever before through the usage of the numerous social media platforms readily available today. Once posted online and released to the public, information is almost impossible to withdraw or delete. More alarmingly, postings may [...] Read more.
Sharing information with the public is becoming easier than ever before through the usage of the numerous social media platforms readily available today. Once posted online and released to the public, information is almost impossible to withdraw or delete. More alarmingly, postings may carry sensitive information far beyond what was intended to be released, so-called incidental data, which raises various additional security and privacy concerns. To improve our understanding of the awareness of incidental data, we conducted a survey where we asked 192 students for their opinions on publishing selected postings on social media. We found that up to 21.88% of all participants would publish a posting that contained incidental data that two-thirds of them found privacy-compromising. Our results show that continued efforts are needed to increase our awareness of incidental data posted on social media. Full article
(This article belongs to the Special Issue Data Protection and Privacy)
►▼ Show Figures

Figure 1

Figure 1
<p>Venn-diagram of the number of enrolled students in the three courses we recruited participants from and how they overlap (<b>left</b>); and the number of responses before and after cleaning (<b>right</b>).</p> Full article ">Figure 2
<p>Shows the pictures presented to participants in first question Q1 and Q1.1. Subfigures (<b>a</b>–<b>c</b>) show different scenes from the video. (<b>a</b>,<b>b</b>) combined hints the shape of the backyard, whereas (<b>c</b>) depicts a smartphone with a weather app showing an incoming storm and the current position as a blue dot. The pictures were taken from Kutschera [<a href="#B6-jcp-04-00006" class="html-bibr">6</a>] and [<a href="#B34-jcp-04-00006" class="html-bibr">34</a>], respectively.</p> Full article ">Figure 3
<p>Shows the pictures presented to participants in second question Q2 and Q2.1. Each subfigure (<b>a</b>–<b>c</b>) represents different postings from the same person on Twitter. The posting shown in (<b>a</b>) is a response to a question if the car is still owned, (<b>b</b>) an untriggered comment about how beautiful the day is, (<b>c</b>) depicts a posting that comments on the end of the day where the skyline and a small pool is visible alongside the moon. The pictures were taken from Kutschera [<a href="#B6-jcp-04-00006" class="html-bibr">6</a>] and Twitter [<a href="#B35-jcp-04-00006" class="html-bibr">35</a>,<a href="#B36-jcp-04-00006" class="html-bibr">36</a>,<a href="#B37-jcp-04-00006" class="html-bibr">37</a>], respectively.</p> Full article ">Figure 4
<p>Depicts the boxplot visualizing the statistical values, such as median and the quantiles of all answers, from Q3.1 to Q3.20.</p> Full article ">Figure 5
<p>Depicts the workflow a student must undertake in order to receive bonus points, how anonymity is preserved, and how data are kept clean and trustworthy. Blue indicates a student action, whereas green indicates a lecturer or researcher role.</p> Full article ">
13 pages, 3271 KiB  
Article
Evaluating Cluster-Based Synthetic Data Generation for Blood-Transfusion Analysis
by Shannon K. S. Kroes, Matthijs van Leeuwen, Rolf H. H. Groenwold and Mart P. Janssen
J. Cybersecur. Priv. 2023, 3(4), 882-894; https://doi.org/10.3390/jcp3040040 - 1 Dec 2023
Cited by 1 | Viewed by 1626
Abstract
Synthetic data generation is becoming an increasingly popular approach to making privacy-sensitive data available for analysis. Recently, cluster-based synthetic data generation (CBSDG) has been proposed, which uses explainable and tractable techniques for privacy preservation. Although the algorithm demonstrated promising performance on simulated data, [...] Read more.
Synthetic data generation is becoming an increasingly popular approach to making privacy-sensitive data available for analysis. Recently, cluster-based synthetic data generation (CBSDG) has been proposed, which uses explainable and tractable techniques for privacy preservation. Although the algorithm demonstrated promising performance on simulated data, CBSDG has not yet been applied to real, personal data. In this work, a published blood-transfusion analysis is replicated with synthetic data to assess whether CBSDG can reproduce more complex and intricate variable relations than previously evaluated. Data from the Dutch national blood bank, consisting of 250,729 donation records, were used to predict donor hemoglobin (Hb) levels by means of support vector machines (SVMs). Precision scores were equal to the original data results for both male (0.997) and female (0.987) donors, recall was 0.007 higher for male and 0.003 lower for female donors (original estimates 0.739 and 0.637, respectively). The impact of the variables on Hb predictions was similar, as quantified and visualized with Shapley additive explanation values. Opportunities for attribute disclosure were decreased for all but two variables; only the binary variables Deferral Status and Sex could still be inferred. Such inference was also possible for donors who were not used as input for the generator and may result from correlations in the data as opposed to overfitting in the synthetic-data-generation process. The high predictive performance obtained with the synthetic data shows potential of CBSDG for practical implementation. Full article
(This article belongs to the Special Issue Data Protection and Privacy)
►▼ Show Figures

Figure 1

Figure 1
<p>Visualization of utility. CBSDG generates synthetic data with a mixed sum-product network (MSPN). With both the synthetic and training data, a support vector machine (SVM) is trained, with which predictions are made for donors in the test data set (indicated with dashed line). These predictions are compared as a measure of utility. The process is repeated 50 times: 50 MPSNs are generated, resulting in 50 synthetic data sets and 100 corresponding SVMs (one per sex).</p> Full article ">Figure 2
<p>Density plots and histograms per variable for the original data (blue, solid line) and for a synthetic data set, chosen at random (red, dashed line).</p> Full article ">Figure 3
<p>Density plots of precision and recall for Hb deferral predictions, using SVMs based on synthetic data, for 50 repetitions (blue for males, orange for females), with corresponding results for the original data (black vertical lines).</p> Full article ">Figure 4
<p>Variable importance (average absolute SHAP values) of the model based on the original data (black dot) and over 50 repetitions for synthetic data (violin plots) for males.</p> Full article ">Figure 5
<p>Variable importance (average absolute SHAP values) of the model based on the original data (black dot) and over 50 repetitions for synthetic data (violin plots) for females.</p> Full article ">

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22 pages, 2224 KiB  
Systematic Review
Humans and Automation: Augmenting Security Operation Centers
by Jack Tilbury and Stephen Flowerday
J. Cybersecur. Priv. 2024, 4(3), 388-409; https://doi.org/10.3390/jcp4030020 - 1 Jul 2024
Cited by 2 | Viewed by 2885
Abstract
The continuous integration of automated tools into security operation centers (SOCs) increases the volume of alerts for security analysts. This amplifies the risk of automation bias and complacency to the point that security analysts have reported missing, ignoring, and not acting upon critical [...] Read more.
The continuous integration of automated tools into security operation centers (SOCs) increases the volume of alerts for security analysts. This amplifies the risk of automation bias and complacency to the point that security analysts have reported missing, ignoring, and not acting upon critical alerts. Enhancing the SOC environment has predominantly been researched from a technical standpoint, failing to consider the socio-technical elements adequately. However, our research fills this gap and provides practical insights for optimizing processes in SOCs. The synergy between security analysts and automation can potentially augment threat detection and response capabilities, ensuring a more robust defense if effective human-automation collaboration is established. A scoping review of 599 articles from four databases led to a final selection of 49 articles. Thematic analysis resulted in 609 coding references generated across four main themes: SOC automation challenges, automation application areas, implications on analysts, and human factor sentiment. Our findings emphasize the extent to which automation can be implemented across the incident response lifecycle. The SOC Automation Matrix represents our primary contribution to achieving a mutually beneficial relationship between analyst and machine. This matrix describes the properties of four distinct human-automation combinations. This is of practical value to SOCs striving to optimize their processes, as our matrix mentions socio-technical system characteristics for automated tools. Full article
(This article belongs to the Special Issue Data Protection and Privacy)
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Figure 1

Figure 1
<p>Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) diagram.</p> Full article ">Figure 2
<p>Number of publications by year.</p> Full article ">Figure 3
<p>Thematic analysis coding strategy result.</p> Full article ">Figure 4
<p>X-AI terminology.</p> Full article ">Figure 5
<p>Automation application areas and system characteristics.</p> Full article ">Figure 6
<p>SOC Automation Matrix.</p> Full article ">Figure 7
<p>Fully implemented system characteristics.</p> Full article ">Figure 8
<p>Partially implemented system characteristics.</p> Full article ">Figure 9
<p>SOC levels of automation.</p> Full article ">Figure A1
<p>The thematic map above displays the SOC challenges that necessitate automation, broken down into four sub-themes.</p> Full article ">Figure A2
<p>The thematic map above displays the SOC automation application areas, broken down into two sub-themes and discussed in terms of the incident response lifecycle.</p> Full article ">
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