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Big Data Security, Privacy and Sustainability
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Big Data Security, Privacy and Sustainability

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 27063

Special Issue Editor

Prof. Dr. Young-Gab Kim

E-Mail Website
Guest Editor
Department of Computer and Security, Sejong University, Seoul 05006, Republic of Korea
Interests: Internet of Things (IoT); security; AI-based video security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With rapid development of communication networks (e.g., 4G, 5G and, social network services) and smart devices (e.g., smartphones, smart sensors, smartwatches, and smart speakers), we have been feeling the rapid development of big data. Accordingly, security and privacy are the most concerned issues in big data. That is, due to the fact that our life have been influenced by big data service, the big data security has become indispensable requirement not only for personal privacy, but also for assuring the sustainability of the security. The security and privacy protection should be considered in all through the storage, transmission and processing of the big data.

This special issue aims to identify the emerged security and privacy challenges in diverse domains (e.g., finance, medical, and public organizations) for the big data. Moreover, it will provide the up-to-date state-of-the-art for the security, privacy and sustainability aspects of the big data.

Topics of primary interest include, but are not limited to:

  • Security, privacy, and sustainability issues in emergent technologies for big data
  • Security, privacy, and sustainability architecture for big data
  • Security, privacy, and sustainability testing methods for big data
  • Security, privacy, and sustainability management for big data
  • Security, privacy challenges and mitigation methods for big data
  • Security policy for big data
  • Case studies experience reports on big data security, privacy, and sustainability

Prof. Young-Gab Kim
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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

  • big data
  • big data security
  • big data privacy
  • sustainability of the big data security
  • emerging security and privacy challenges

Published Papers (4 papers)

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Research

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19 pages, 4083 KiB  
Article
C4I System Security Architecture: A Perspective on Big Data Lifecycle in a Military Environment
by Seungjin Baek and Young-Gab Kim
Sustainability 2021, 13(24), 13827; https://0-doi-org.brum.beds.ac.uk/10.3390/su132413827 - 14 Dec 2021
Cited by 3 | Viewed by 3230
Abstract
Although the defense field is also one of the key areas that use big data for security reasons, there is a lack of study that designs system frameworks and presents security requirements to implement big data in defense. However, we overcome the security [...] Read more.
Although the defense field is also one of the key areas that use big data for security reasons, there is a lack of study that designs system frameworks and presents security requirements to implement big data in defense. However, we overcome the security matters by examining the battlefield environment and the system through the flow of data in the battlefield. As such, this research was conducted to apply big data in the defense domain, which is a unique field. In particular, a three-layered system framework was designed to apply big data in the C4I system, which collects, manages, and analyzes data generated from the battlefield, and the security measures required for each layer were developed. First, to enhance the general understanding of big data and the military environment, an overview of the C4I system, the characteristics of the 6V’s, and the five-phase big data lifecycle were described. While presenting a framework that divides the C4I system into three layers, the roles and components of each layer are described in detail, considering the big data lifecycle and system framework. A security architecture is finally proposed by specifying security requirements for each field in the three-layered C4I system. The proposed system framework and security architecture more accurately explain the unique nature of the military domain than those studied in healthcare, smart grids, and smart cities; development directions requiring further research are described. Full article
(This article belongs to the Special Issue Big Data Security, Privacy and Sustainability)
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19 pages, 23625 KiB  
Article
Real-Time DDoS Attack Detection System Using Big Data Approach
by Mazhar Javed Awan, Umar Farooq, Hafiz Muhammad Aqeel Babar, Awais Yasin, Haitham Nobanee, Muzammil Hussain, Owais Hakeem and Azlan Mohd Zain
Sustainability 2021, 13(19), 10743; https://0-doi-org.brum.beds.ac.uk/10.3390/su131910743 - 27 Sep 2021
Cited by 99 | Viewed by 9088
Abstract
Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows up in various shapes and patterns, therefore it is not easy to detect and solve with previous solutions. Classification algorithms have been used in many studies and have aimed to [...] Read more.
Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows up in various shapes and patterns, therefore it is not easy to detect and solve with previous solutions. Classification algorithms have been used in many studies and have aimed to detect and solve the DDoS attack. DDoS attacks are performed easily by using the weaknesses of networks and by generating requests for services for software. Real-time detection of DDoS attacks is difficult to detect and mitigate, but this solution holds significant value as these attacks can cause big issues. This paper addresses the prediction of application layer DDoS attacks in real-time with different machine learning models. We applied the two machine learning approaches Random Forest (RF) and Multi-Layer Perceptron (MLP) through the Scikit ML library and big data framework Spark ML library for the detection of Denial of Service (DoS) attacks. In addition to the detection of DoS attacks, we optimized the performance of the models by minimizing the prediction time as compared with other existing approaches using big data framework (Spark ML). We achieved a mean accuracy of 99.5% of the models both with and without big data approaches. However, in training and testing time, the big data approach outperforms the non-big data approach due to that the Spark computations in memory are in a distributed manner. The minimum average training and testing time in minutes was 14.08 and 0.04, respectively. Using a big data tool (Apache Spark), the maximum intermediate training and testing time in minutes was 34.11 and 0.46, respectively, using a non-big data approach. We also achieved these results using the big data approach. We can detect an attack in real-time in few milliseconds. Full article
(This article belongs to the Special Issue Big Data Security, Privacy and Sustainability)
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25 pages, 670 KiB  
Article
Data Usage and Access Control in Industrial Data Spaces: Implementation Using FIWARE
by Andres Munoz-Arcentales, Sonsoles López-Pernas, Alejandro Pozo, Álvaro Alonso, Joaquín Salvachúa and Gabriel Huecas
Sustainability 2020, 12(9), 3885; https://0-doi-org.brum.beds.ac.uk/10.3390/su12093885 - 09 May 2020
Cited by 16 | Viewed by 4789
Abstract
In recent years, a new business paradigm has emerged which revolves around effectively extracting value from data. In this scope, providing a secure ecosystem for data sharing that ensures data governance and traceability is of paramount importance as it holds the potential to [...] Read more.
In recent years, a new business paradigm has emerged which revolves around effectively extracting value from data. In this scope, providing a secure ecosystem for data sharing that ensures data governance and traceability is of paramount importance as it holds the potential to create new applications and services. Protecting data goes beyond restricting who can access what resource (covered by identity and Access Control): it becomes necessary to control how data are treated once accessed, which is known as data Usage Control. Data Usage Control provides a common and trustful security framework to guarantee the compliance with data governance rules and responsible use of organizations’ data by third-party entities, easing and ensuring secure data sharing in ecosystems such as Smart Cities and Industry 4.0. In this article, we present an implementation of a previously published architecture for enabling access and Usage Control in data-sharing ecosystems among multiple organizations using the FIWARE European open source platform. Additionally, we validate this implementation through a real use case in the food industry. We conclude that the proposed model, implemented using FIWARE components, provides a flexible and powerful architecture to manage Usage Control in data-sharing ecosystems. Full article
(This article belongs to the Special Issue Big Data Security, Privacy and Sustainability)
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Review

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32 pages, 4066 KiB  
Review
Security and Privacy in Big Data Life Cycle: A Survey and Open Challenges
by Jahoon Koo, Giluk Kang and Young-Gab Kim
Sustainability 2020, 12(24), 10571; https://0-doi-org.brum.beds.ac.uk/10.3390/su122410571 - 17 Dec 2020
Cited by 36 | Viewed by 7735
Abstract
The use of big data in various fields has led to a rapid increase in a wide variety of data resources, and various data analysis technologies such as standardized data mining and statistical analysis techniques are accelerating the continuous expansion of the big [...] Read more.
The use of big data in various fields has led to a rapid increase in a wide variety of data resources, and various data analysis technologies such as standardized data mining and statistical analysis techniques are accelerating the continuous expansion of the big data market. An important characteristic of big data is that data from various sources have life cycles from collection to destruction, and new information can be derived through analysis, combination, and utilization. However, each phase of the life cycle presents data security and reliability issues, making the protection of personally identifiable information a critical objective. In particular, user tendencies can be analyzed using various big data analytics, and this information leads to the invasion of personal privacy. Therefore, this paper identifies threats and security issues that occur in the life cycle of big data by confirming the current standards developed by international standardization organizations and analyzing related studies. In addition, we divide a big data life cycle into five phases (i.e., collection, storage, analytics, utilization, and destruction), and define the security taxonomy of the big data life cycle based on the identified threats and security issues. Full article
(This article belongs to the Special Issue Big Data Security, Privacy and Sustainability)
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