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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: 30 June 2024 | Viewed by 2236

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

Published Papers (2 papers)

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Research

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
Viewed by 790
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)
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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 - 01 Dec 2023
Viewed by 890
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)
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