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Future Internet
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Security and Privacy Issues in the Internet of Cloud
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Security and Privacy Issues in the Internet of Cloud

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 8884

Special Issue Editor

Prof. Dr. Christos Kalloniatis

E-Mail Website
Guest Editor
Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece
Interests: privacy requirements engineering; security requirements engineering; business modelling; security and privacy in cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cloud computing services have dominated a great amount of Internet resources for many years since most everyday Internet users take advantage of the various types of services offered through the three service models provided. In recent years, beside private users we have witnessed many enterprises, organizations, and public sector governments adopt cloud-based solutions to improve their daily operations and offer new services to users and third party organizations.

In parallel with this, based on recent reports one the fastest developing domains of cloud computing infrastructure is the Internet of Things (IoT). In 2021, American enterprises alone increased IoT-based solutions by 44% compared to 2020. This combination of Cloud Computing and IoT has led the research community to adopt the term Internet of Cloud (IoC), since it is a new domain that raises plenty of opportunities and challenges but also questions and issues demanding deeper investigation.

This Special Issue will discuss this trending topic and specifically the security and privacy challenges raised in this new area.

Prof. Dr. Christos Kalloniatis
Guest Editor

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 challenges for IoC
  • privacy challenges for IoC
  • IoC and edge computing challenges
  • ethical issues with IoC
  • security and privacy requirements of IoC
  • security solutions and PETs in IoC

Published Papers (3 papers)

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Research

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25 pages, 1738 KiB  
Article
Federated Adversarial Training Strategies for Achieving Privacy and Security in Sustainable Smart City Applications
by Sapdo Utomo, Adarsh Rouniyar, Hsiu-Chun Hsu and Pao-Ann Hsiung
Future Internet 2023, 15(11), 371; https://doi.org/10.3390/fi15110371 - 20 Nov 2023
Viewed by 3004
Abstract
Smart city applications that request sensitive user information necessitate a comprehensive data privacy solution. Federated learning (FL), also known as privacy by design, is a new paradigm in machine learning (ML). However, FL models are susceptible to adversarial attacks, similar to other AI [...] Read more.
Smart city applications that request sensitive user information necessitate a comprehensive data privacy solution. Federated learning (FL), also known as privacy by design, is a new paradigm in machine learning (ML). However, FL models are susceptible to adversarial attacks, similar to other AI models. In this paper, we propose federated adversarial training (FAT) strategies to generate robust global models that are resistant to adversarial attacks. We apply two adversarial attack methods, projected gradient descent (PGD) and the fast gradient sign method (FGSM), to our air pollution dataset to generate adversarial samples. We then evaluate the effectiveness of our FAT strategies in defending against these attacks. Our experiments show that FGSM-based adversarial attacks have a negligible impact on the accuracy of global models, while PGD-based attacks are more effective. However, we also show that our FAT strategies can make global models robust enough to withstand even PGD-based attacks. For example, the accuracy of our FAT-PGD and FL-mixed-PGD models is 81.13% and 82.60%, respectively, compared to 91.34% for the baseline FL model. This represents a reduction in accuracy of 10%, but this could be potentially mitigated by using a more complex and larger model. Our results demonstrate that FAT can enhance the security and privacy of sustainable smart city applications. We also show that it is possible to train robust global models from modest datasets per client, which challenges the conventional wisdom that adversarial training requires massive datasets. Full article
(This article belongs to the Special Issue Security and Privacy Issues in the Internet of Cloud)
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25 pages, 444 KiB  
Article
Privacy Goals for the Data Lifecycle
by Jane Henriksen-Bulmer, Cagatay Yucel, Shamal Faily and Ioannis Chalkias
Future Internet 2022, 14(11), 315; https://doi.org/10.3390/fi14110315 - 31 Oct 2022
Viewed by 1704
Abstract
The introduction of Data Protection by Default and Design (DPbDD) brought in as part of the General Data Protection Regulation (GDPR) in 2018, has necessitated that businesses review how best to incorporate privacy into their processes in a transparent manner, so as to [...] Read more.
The introduction of Data Protection by Default and Design (DPbDD) brought in as part of the General Data Protection Regulation (GDPR) in 2018, has necessitated that businesses review how best to incorporate privacy into their processes in a transparent manner, so as to build trust and improve decisions around privacy best practice. To address this issue, this paper presents a 7-stage data lifecycle, supported by nine privacy goals that together, will help practitioners manage data holdings throughout data lifecycle. The resulting data lifecycle (7-DL) was created as part of the Ideal-Cities project, a Horizon-2020 Smart-city initiative, that seeks to facilitate data re-use and/or repurposed. We evaluate 7-DL through peer review and an exemplar worked example that applies the data lifecycle to a real-time life logging fire incident scenario, one of the Ideal-Cities use cases to demonstrate the applicability of the framework. Full article
(This article belongs to the Special Issue Security and Privacy Issues in the Internet of Cloud)
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Review

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35 pages, 2576 KiB  
Review
Review on Semantic Modeling and Simulation of Cybersecurity and Interoperability on the Internet of Underwater Things
by Konstantinos Kotis, Stavros Stavrinos and Christos Kalloniatis
Future Internet 2023, 15(1), 11; https://doi.org/10.3390/fi15010011 - 26 Dec 2022
Cited by 5 | Viewed by 3016
Abstract
As maritime and military missions become more and more complex and multifactorial over the years, there has been a high interest in the research and development of (autonomous) unmanned underwater vehicles (UUVs). Latest efforts concern the modeling and simulation of UUVs’ collaboration in [...] Read more.
As maritime and military missions become more and more complex and multifactorial over the years, there has been a high interest in the research and development of (autonomous) unmanned underwater vehicles (UUVs). Latest efforts concern the modeling and simulation of UUVs’ collaboration in swarm formations, towards obtaining deeper insights related to the critical issues of cybersecurity and interoperability. The research topics, which are constantly emerging in this domain, are closely related to the communication, interoperability, and secure operation of UUVs, as well as to the volume, velocity, variety, and veracity of data transmitted in low bit-rate due to the medium, i.e., the water. This paper reports on specific research topics in the domain of UUVs, emphasizing interoperability and cybersecurity in swarms of UUVs in a military/search-and-rescue setting. The goal of this work is two-fold: a) to review existing methods and tools of semantic modeling and simulation for cybersecurity and interoperability on the Internet of Underwater Things (IoUT), b) to highlight open issues and challenges, towards developing a novel simulation approach to effectively support critical and life-saving decision-making of commanders of military and search-and-rescue operations. Full article
(This article belongs to the Special Issue Security and Privacy Issues in the Internet of Cloud)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Federated Adversarial Training Strategies for Achieving Privacy and Security in Sustainable Smart City Applications
Authors: Sapdo Utomo; Adarsh Rouniyar; Hsiu-Chun Hsu; Pao-Ann Hsiung
Affiliation: National Chung Cheng University, Taiwan, R.O.C.
Abstract: As promoted by the United Nations, the Sustainable Development Goals (SDG) have recently the foundation of research frameworks among academics and researchers. SDG has a total of 17 goals, of which SDG 11 "Sustainable Cities and Communities," concentrates on the global development of sustainable cities. High technology, including the Internet of Things (IoT), the Internet of Clouds (IoC), artificial intelligence, smart monitoring systems, etc., plays an important role in attaining SDG 11 goals. However, as more cases of data breaches occur, the demand for privacy protection increases. The increasing number of applications in smart cities that may request sensitive user data necessitates a comprehensive solution to safeguard user data privacy. Federated learning, also referred to as privacy by design, has emerged in the field of machine learning. Users' privacy can be protected by keeping their data on local devices (federated clients). Our experiments indicate that federated learning can produce a global model with better performance compared to a local model (the client model). This indicates that the process of aggregation in federated learning can help all clients attain a superior model. In addition, by storing data on clients, it is possible to avoid the network congestion issues that frequently plague centralized systems. Nevertheless, AI models are vulnerable to adversarial attack, and federated learning is no exception, leaving the door open for security concerns. We propose comprehensive, federated adversarial training strategies to address this issue. For adversarial training, FGSM and PGD adversarial examples will be generated from the original dataset. Our strategies will be applied to well-known datasets such as MNIST and CIFAR10, in addition to our own publicly available Air Pollution dataset on Kaggle, to demonstrate the viability of this solution in real-world scenarios. Our work presents robust models for ensuring a high level of security and protecting the privacy of data in applications for a sustainable smart city. Federated learning is also closely related to the IoT and IoC domains, which can be synergized to produce enhanced solutions. Keywords: sustainable smart cities; federated learning; adversarial attack; privacy protection; robust model

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