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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: closed (31 December 2024) | Viewed by 21888

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 requirement engineering; security requirement 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

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Related Special Issue

Published Papers (5 papers)

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Research

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24 pages, 4357 KiB  
Article
Investigation of Smart Machines with DNAs in SpiderNet
by Mo Adda and Nancy Scheidt
Future Internet 2025, 17(2), 92; https://doi.org/10.3390/fi17020092 - 17 Feb 2025
Viewed by 574
Abstract
The advancement of Internet of Things (IoT), robots, drones, and vehicles signifies ongoing progress, accompanied by increasing complexities and challenges in forensic investigations. Globally, investigators encounter obstacles when extracting evidence from these vast landscapes, which include diverse devices, networks, and cloud environments. Of [...] Read more.
The advancement of Internet of Things (IoT), robots, drones, and vehicles signifies ongoing progress, accompanied by increasing complexities and challenges in forensic investigations. Globally, investigators encounter obstacles when extracting evidence from these vast landscapes, which include diverse devices, networks, and cloud environments. Of particular concern is the process of evidence collection, especially regarding fingerprints and facial recognition within the realm of vehicle forensics. Moreover, ensuring the integrity of forensic evidence is a critical issue, as it is vulnerable to attacks targeting data centres and server farms. Mitigating these challenges, along with addressing evidence mobility, presents additional complexities. This paper introduces a groundbreaking infrastructure known as SpiderNet, which is based on cloud computing principles. We will illustrate how this architecture facilitates the identification of devices, secures the integrity of evidence both at its source and during transit, and enables investigations into individuals involved in criminal activities. Through case studies, we will demonstrate the potential of SpiderNet to assist law enforcement agencies in addressing crimes perpetrated within IoT environments. Full article
(This article belongs to the Special Issue Security and Privacy Issues in the Internet of Cloud)
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23 pages, 273 KiB  
Article
Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
by Kyungmin Sim, Honyeong Heo and Haehyun Cho
Future Internet 2024, 16(10), 363; https://doi.org/10.3390/fi16100363 - 5 Oct 2024
Cited by 2 | Viewed by 7379
Abstract
Behind everyday websites, a hidden shadow world tracks the behavior of Internet users. Web tracking analyzes online activity based on collected data and delivers content tailored to users’ interests. It gathers vast amounts of information for various purposes, ranging from sensitive personal data [...] Read more.
Behind everyday websites, a hidden shadow world tracks the behavior of Internet users. Web tracking analyzes online activity based on collected data and delivers content tailored to users’ interests. It gathers vast amounts of information for various purposes, ranging from sensitive personal data to seemingly minor details such as IP addresses, devices, browsing histories, settings, and preferences. While Web tracking is largely a legitimate technology, the increase in illegal user tracking, data breaches, and the unlawful sale of data has become a growing concern. As a result, the demand for technologies that can detect and prevent Web trackers is more important than ever. This paper provides an overview of Web tracking technologies, relevant research, and website measurement tools designed to identify web-based tracking. It also explores technologies for preventing Web tracking and discusses potential directions for future research. Full article
(This article belongs to the Special Issue Security and Privacy Issues in the Internet of Cloud)
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
Cited by 2 | Viewed by 4650
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
Cited by 3 | Viewed by 2687
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 11 | Viewed by 4764
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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