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Future Internet
Special Issues
Privacy and Security in Computing Continuum and Data-Driven Workflows
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Privacy and Security in Computing Continuum and Data-Driven Workflows

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 5957

Special Issue Editors

Dr. Thomas Loruenser

E-Mail Website
Guest Editor
AIT Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria
Interests: (quantum) cryptography; network security; distributed systems; cloud security; IoT security
Dr. Stephan Krenn

E-Mail Website
Guest Editor
AIT Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria
Interests: IT security; encryption; authentication; applied cryptography; information privacy; network security

Special Issue Information

Dear Colleagues,

The current technological progress in ICT as well as the demand for digital transformation drive the development of novel networks, systems, and platforms, which in turn enables collaboration and data-driven workflows on a large scale and beyond existing trust boundaries.

The ongoing seamless integration of cloud and edge computing as well as the Internet of Things (IoT) drive this change towards the so-called computing continuum, which forms the basis for all emerging digital data and collaboration spaces.

However, many new and significant security and privacy risks have to be taken into consideration with such systems. Given the vast amount of potentially sensitive information being processed and the high number of involved entities and devices with different profiles (e.g., available resources, capabilities), protection of confidentiality throughout the entire data life cycle in the computing continuum is a major challenge. Similarly, due to the increasing number of services and devices being added, protecting authenticity from end to end is also highly important. Thus, novel solutions and approaches for secure and privacy-preserving data sharing and processing will be key to unleash the potential of the computing continuum while still ensuring data sovereignty.

This Special Issue is dedicated to research on methods, technologies, and novel approaches focused on increasing security and privacy protection for data and users. We are calling for cutting-edge contributions to fundamental theoretical research as well as applications in practice.

Dr. Thomas Loruenser
Dr. Stephan Krenn
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • security and privacy for data spaces
  • privacy friendly data markets and platforms
  • secure data-driven workflows
  • IoT and cyber-physical security and/or privacy
  • online privacy and anonymous authentication
  • data sovereignty
  • cloud and edge computing security
  • privacy-enhancing technologies
  • secure computing technologies
  • privacy preserving machine learning
  • computing on encrypted data
  • hardware security and attestation
  • End2end security in IoT and cloud computing
  • verifiable computing
  • End2end authenticity

Published Papers (2 papers)

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Research

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16 pages, 743 KiB  
Article
FREDY: Federated Resilience Enhanced with Differential Privacy
by Zacharias Anastasakis, Terpsichori-Helen Velivassaki, Artemis Voulkidis, Stavroula Bourou, Konstantinos Psychogyios, Dimitrios Skias and Theodore Zahariadis
Future Internet 2023, 15(9), 296; https://doi.org/10.3390/fi15090296 - 01 Sep 2023
Viewed by 1025
Abstract
Federated Learning is identified as a reliable technique for distributed training of ML models. Specifically, a set of dispersed nodes may collaborate through a federation in producing a jointly trained ML model without disclosing their data to each other. Each node performs local [...] Read more.
Federated Learning is identified as a reliable technique for distributed training of ML models. Specifically, a set of dispersed nodes may collaborate through a federation in producing a jointly trained ML model without disclosing their data to each other. Each node performs local model training and then shares its trained model weights with a server node, usually called Aggregator in federated learning, as it aggregates the trained weights and then sends them back to its clients for another round of local training. Despite the data protection and security that FL provides to each client, there are still well-studied attacks such as membership inference attacks that can detect potential vulnerabilities of the FL system and thus expose sensitive data. In this paper, in order to prevent this kind of attack and address private data leakage, we introduce FREDY, a differential private federated learning framework that enables knowledge transfer from private data. Particularly, our approach has a teachers–student scheme. Each teacher model is trained on sensitive, disjoint data in a federated manner, and the student model is trained on the most voted predictions of the teachers on public unlabeled data which are noisy aggregated in order to guarantee the privacy of each teacher’s sensitive data. Only the student model is publicly accessible as the teacher models contain sensitive information. We show that our proposed approach guarantees the privacy of sensitive data against model inference attacks while it combines the federated learning settings for the model training procedures. Full article
(This article belongs to the Special Issue Privacy and Security in Computing Continuum and Data-Driven Workflows)
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Review

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25 pages, 374 KiB  
Review
Dynamic Risk Assessment in Cybersecurity: A Systematic Literature Review
by Pavlos Cheimonidis and Konstantinos Rantos
Future Internet 2023, 15(10), 324; https://doi.org/10.3390/fi15100324 - 28 Sep 2023
Cited by 2 | Viewed by 2514
Abstract
Traditional information security risk assessment (RA) methodologies and standards, adopted by information security management systems and frameworks as a foundation stone towards robust environments, face many difficulties in modern environments where the threat landscape changes rapidly and new vulnerabilities are being discovered. In [...] Read more.
Traditional information security risk assessment (RA) methodologies and standards, adopted by information security management systems and frameworks as a foundation stone towards robust environments, face many difficulties in modern environments where the threat landscape changes rapidly and new vulnerabilities are being discovered. In order to overcome this problem, dynamic risk assessment (DRA) models have been proposed to continuously and dynamically assess risks to organisational operations in (near) real time. The aim of this work is to analyse the current state of DRA models that have been proposed for cybersecurity, through a systematic literature review. The screening process led us to study 50 DRA models, categorised based on the respective primary analysis methods they used. The study provides insights into the key characteristics of these models, including the maturity level of the examined models, the domain or application area in which these models flourish, and the information they utilise in order to produce results. The aim of this work is to answer critical research questions regarding the development of dynamic risk assessment methodologies and provide insights on the already developed methods as well as future research directions. Full article
(This article belongs to the Special Issue Privacy and Security in Computing Continuum and Data-Driven Workflows)
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