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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (1 May 2022) | Viewed by 73359

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Prof. Dr. Abdul Razaque

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Department of Computer Engineering and Information Security, International Information Technology University, Almaty, Kazakhstan
Interests: big data; data mining; machine learning; WSNs; cloud computing; network security; ambient intelligence
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Dr. Syed Rizvi

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Information Sciences and Technology, The Pennsylvania State University, State College, PA 16801, USA
Interests: network security; cybersecurity; secure cloud computing; IoT; big data; machine learning
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Dr. Mohamed Ben Haj Frej

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Department of Computer Science & Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
Interests: neural network; cybersecurity; secure cloud computing; IoT; big data; machine learning
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Dr. Abrar Alajlan

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Guest Editor

Common First Year Deanship, King Saud University, Riyadh 11421, Saudi Arabia
Interests: neural network; cybersecurity; secure cloud computing; IoT; big data; machine learning

Dr. Fathi Amsaad

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Guest Editor

School of Information Security and Applied Computing, College of Engineering & Technology, Eastern Michigan University, Ypsilanti, MI 48197, USA
Interests: microelectronics/hardware assisted security; emerging IoT and connected autonomous systems security; security and privacy of smart building and spaces in modern smart cities environment; trusted next generations smart power grid networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The emergence of big data technology offers cost-effective predictions to improve the decision-making capabilities in highly critical domains such as crime, security, employment, healthcare, insurance, resource management, and natural disaster. Big data can be used to leverage significant features of IoT devices to provide expeditious mapping of interconnectivity for companies, the media industry, and the government to more precisely target their audience and augment efficiency. Currently, big data has had great implications during the COVID-19 pandemic situation for minimizing the severe impact of the disease and introducing the medical treatment process. However, big data has been a great challenge in terms of privacy preservation. Thus, the main goal of this Special Issue is to invite high-quality submissions in the form of original and novel research articles on big data privacy, privacy-preserving aggregation, privacy concerns of big data technology, the impact of big data on COVID-19, identity-based big data privacy, and privacy-preservation of Big data storage on the cloud. In addition, attention will also be given to several big data industry-driven applications.

Prof. Abdul Razaque
Dr. Syed Rizvi
Dr. Mohamed Ben Haj Frej
Dr. Abrar Alajlan
Dr. Fathi Amsaad
Guest Editors

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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. Electronics 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 privacy-preservation models
Big data privacy preservation for support of legal measures and industry specifications
Emerging standards for big data privacy preservation
Impact of big data on COVID-19
COVID-19 privacy data protection
Privacy-preserving models in big data (e.g., K-anonymity, L-diversity, T-closeness, and δ-presence, etc.)
Big data publishing differential privacy
Integration of big data and IoT
Big data principles for machine intelligence
Infrastructure security and impact of big data privacy
Privacy aspects of big data in healthcare and other industries.
Big data privacy-preserving models for social networks
Big data privacy anonymization
Privacy of big data-intensive technologies

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Published Papers (12 papers)

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Research

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14 pages, 1732 KiB

Open AccessArticle

Securing Drug Distribution Systems from Tampering Using Blockchain

by Mamoona Humayun, Noor Zaman Jhanjhi, Mahmood Niazi, Fathi Amsaad and Isma Masood

Electronics 2022, 11(8), 1195; https://doi.org/10.3390/electronics11081195 - 9 Apr 2022

Cited by 30 | Viewed by 6390

Abstract

The purpose of this study is to overcome coordination flaws and enhance end-to-end security in the drug distribution market (DDM). One of the major issues in drug market coordination management is the absence of a centralized monitoring system to provide adequate market control and offer real-time prices, availability, and authentication data. Further, tampering is another serious issue affecting the DDM, and as a consequence, there is a significant global market for counterfeit drugs. This vast counterfeit drug business presents a security risk to the distribution system. This study presents a blockchain-based solution to challenges such as coordination failure, secure drug delivery, and pharmaceutical authenticity. To optimize the drug distribution process (DDP), a framework for drug distribution is presented. The proposed framework is evaluated using mathematical modeling and a real-life case study. According to our results, the proposed technique helps to maintain market equilibrium by guaranteeing that there is adequate demand while maintaining supply. Using the suggested framework, massive data created by the medication supply chain would be appropriately handled, allowing market forces to be better regulated and no manufactured shortages to inflate medicine prices. The proposed framework calls for the Drug Regulatory Authority (DRA) to authenticate users on blockchain and to monitor end-to-end DDP. Using the proposed framework, big data generated through drug supply chain will be properly managed; thus, market forces will be better controlled, and no artificial shortages will be generated to raise drug costs. Full article

(This article belongs to the Special Issue Big Data Privacy-Preservation)

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20 pages, 2236 KiB

Open AccessArticle

Big Data Handling Approach for Unauthorized Cloud Computing Access

by Abdul Razaque, Nazerke Shaldanbayeva, Bandar Alotaibi, Munif Alotaibi, Akhmetov Murat and Aziz Alotaibi

Electronics 2022, 11(1), 137; https://doi.org/10.3390/electronics11010137 - 3 Jan 2022

Cited by 14 | Viewed by 3445

Abstract

Nowadays, cloud computing is one of the important and rapidly growing services; its capabilities and applications have been extended to various areas of life. Cloud computing systems face many security issues, such as scalability, integrity, confidentiality, unauthorized access, etc. An illegitimate intruder may gain access to a sensitive cloud computing system and use the data for inappropriate purposes, which may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for big data in cloud computing. The HUDH scheme aims to restrict illegitimate users from accessing the cloud and to provide data security provisions. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. The HUDH scheme involves three algorithms: advanced encryption standards (AES) for encryption, attribute-based access control (ABAC) for data access control, and hybrid intrusion detection (HID) for unauthorized access detection. The proposed scheme is implemented using the Python and Java languages. The testing results demonstrated that the HUDH scheme can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% accuracy. Full article

(This article belongs to the Special Issue Big Data Privacy-Preservation)

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15 pages, 976 KiB

Open AccessArticle

Data Mining to Identify Anomalies in Public Procurement Rating Parameters

by Yeferson Torres-Berru and Vivian F. López Batista

Electronics 2021, 10(22), 2873; https://doi.org/10.3390/electronics10222873 - 22 Nov 2021

Cited by 5 | Viewed by 2661

Abstract

The awarding of public procurement processes is one of the main causes of corruption in governments, due to the fact that in many cases, contracts are awarded to previously agreed suppliers (favouritism); for this selection, the qualification parameters of a process play a fundamental role, seeing as due to their manipulation, bidders with high prices win, causing prejudice to the state. This study identifies processes with anomalies and generates a model for detecting possible corruption in the assignment of process qualification parameters in public procurement. A multi-phase model was used (the identification of anomalies and generation of the detection model), which uses different algorithms, such as clustering (K-Means), Self-Organizing map (SOM), Support Vector Machine (SVM) and Principal Component Analysis (PCA). SOM was used to determine the level of influence of each rating parameter, K-Means to create groups by clustering, semi-supervised learning with SVM and PCA to generate a model to detect anomalies in the processes. By means of a case study, four groups of processes were obtained, highlighting the presence of the group “null economic offer” where the values for the economic offer do not exceed 1%, and a greater weight is given to other qualification parameters, which include direct contracting. The processes in this cluster are considered anomalous. Following this methodology, a semi-supervised learning model is built for the detection of anomalies, which obtains an accuracy of 95%, allowing the detection of procedures where the aim is to benefit a particular supplier by means of the qualification assignment parameters. Full article

(This article belongs to the Special Issue Big Data Privacy-Preservation)

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17 pages, 2024 KiB

Open AccessArticle

Influence of COVID-19 Epidemic on Dark Web Contents

by Abdul Razaque, Bakhytzhan Valiyev, Bandar Alotaibi, Munif Alotaibi, Saule Amanzholova and Aziz Alotaibi

Electronics 2021, 10(22), 2744; https://doi.org/10.3390/electronics10222744 - 10 Nov 2021

Cited by 4 | Viewed by 3561

Abstract

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA) in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net. Full article

(This article belongs to the Special Issue Big Data Privacy-Preservation)

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20 pages, 2342 KiB

Open AccessArticle

Hybrid AES-ECC Model for the Security of Data over Cloud Storage

by Saba Rehman, Nida Talat Bajwa, Munam Ali Shah, Ahmad O. Aseeri and Adeel Anjum

Electronics 2021, 10(21), 2673; https://doi.org/10.3390/electronics10212673 - 31 Oct 2021

Cited by 27 | Viewed by 13403

Abstract

A cloud computing environment provides a cost-effective way for the end user to store and access private data over remote storage using some Internet connection. The user has access to the data anywhere and at any time. However, the data over the cloud do not remain secure all the time. Since the data are accessible to the end user only by using the interference of a third party, it is prone to breach of authentication and integrity of the data. Moreover, cloud computing allows simultaneous users to access and retrieve their data online over different Internet connections, which leads to the exposure, leakage, and loss of a user’s sensitive data in different locations. Many algorithms and protocols have been developed to maintain the security and integrity of the data using cryptographic algorithms such as the Elliptic Curve Cryptography (ECC). This paper proposes a secure and optimized scheme for sharing data while maintaining data security and integrity over the cloud. The proposed system mainly functions by combining the ECC and the Advanced Encryption Standard (AES) method to ensure authentication and data integrity. The experimental results show that the proposed approach is efficient and yields better results when compared with existing approaches. Full article

(This article belongs to the Special Issue Big Data Privacy-Preservation)

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14 pages, 2007 KiB

Open AccessArticle

A Multichannel Deep Learning Framework for Cyberbullying Detection on Social Media

by Munif Alotaibi, Bandar Alotaibi and Abdul Razaque

Electronics 2021, 10(21), 2664; https://doi.org/10.3390/electronics10212664 - 31 Oct 2021

Cited by 56 | Viewed by 6502

Abstract

Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The proposed method achieved promising results. The accuracy of the proposed method was approximately 88%. Full article

(This article belongs to the Special Issue Big Data Privacy-Preservation)

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18 pages, 2816 KiB

Open AccessArticle

k-NDDP: An Efficient Anonymization Model for Social Network Data Release

by Shafaq Shakeel, Adeel Anjum, Alia Asheralieva and Masoom Alam

Electronics 2021, 10(19), 2440; https://doi.org/10.3390/electronics10192440 - 8 Oct 2021

Cited by 7 | Viewed by 2246

Abstract

With the evolution of Internet technology, social networking sites have gained a lot of popularity. People make new friends, share their interests, experiences in life, etc. With these activities on social sites, people generate a vast amount of data that is analyzed by third parties for various purposes. As such, publishing social data without protecting an individual’s private or confidential information can be dangerous. To provide privacy protection, this paper proposes a new degree anonymization approach k-NDDP, which extends the concept of k-anonymity and differential privacy based on Node DP for vertex degrees. In particular, this paper considers identity disclosures on social data. If the adversary efficiently obtains background knowledge about the victim’s degree and neighbor connections, it can re-identify its victim from the social data even if the user’s identity is removed. The contribution of this paper is twofold. First, a simple and, at the same time, effective method k–NDDP is proposed. The method is the extension of k-NMF, i.e., the state-of-the-art method to protect against mutual friend attack, to defend against identity disclosures by adding noise to the social data. Second, the achieved privacy using the concept of differential privacy is evaluated. An extensive empirical study shows that for different values of k, the divergence produced by k-NDDP for CC, BW and APL is not more than

0.8 %

, also added dummy links are

60 %

less, as compared to k-NMF approach, thereby it validates that the proposed k-NDDP approach provides strong privacy while maintaining the usefulness of data. Full article

(This article belongs to the Special Issue Big Data Privacy-Preservation)

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15 pages, 2369 KiB

Open AccessArticle

Suicide Bomb Attack Identification and Analytics through Data Mining Techniques

by Faria Ferooz, Malik Tahir Hassan, Mazhar Javed Awan, Haitham Nobanee, Maryam Kamal, Awais Yasin and Azlan Mohd Zain

Electronics 2021, 10(19), 2398; https://doi.org/10.3390/electronics10192398 - 30 Sep 2021

Cited by 15 | Viewed by 5141

Abstract

Suicide bomb attacks are a high priority concern nowadays for every country in the world. They are a massively destructive criminal activity known as terrorism where one explodes a bomb attached to himself or herself, usually in a public place, taking the lives of many. Terrorist activity in different regions of the world depends and varies according to geopolitical situations and significant regional factors. There has been no significant work performed previously by utilizing the Pakistani suicide attack dataset and no data mining-based solutions have been given related to suicide attacks. This paper aims to contribute to the counterterrorism initiative for the safety of this world against suicide bomb attacks by extracting hidden patterns from suicidal bombing attack data. In order to analyze the psychology of suicide bombers and find a correlation between suicide attacks and the prediction of the next possible venue for terrorist activities, visualization analysis is performed and data mining techniques of classification, clustering and association rule mining are incorporated. For classification, Naïve Bayes, ID3 and J48 algorithms are applied on distinctive selected attributes. The results exhibited by classification show high accuracy against all three algorithms applied, i.e., 73.2%, 73.8% and 75.4%. We adapt the K-means algorithm to perform clustering and, consequently, the risk of blast intensity is identified in a particular location. Frequent patterns are also obtained through the Apriori algorithm for the association rule to extract the factors involved in suicide attacks. Full article

(This article belongs to the Special Issue Big Data Privacy-Preservation)

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15 pages, 3796 KiB

Open AccessArticle

Fake News Data Exploration and Analytics

by Mazhar Javed Awan, Awais Yasin, Haitham Nobanee, Ahmed Abid Ali, Zain Shahzad, Muhammad Nabeel, Azlan Mohd Zain and Hafiz Muhammad Faisal Shahzad

Electronics 2021, 10(19), 2326; https://doi.org/10.3390/electronics10192326 - 23 Sep 2021

Cited by 35 | Viewed by 6630

Abstract

Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models; random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications. Full article

(This article belongs to the Special Issue Big Data Privacy-Preservation)

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17 pages, 3611 KiB

Open AccessArticle

A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach

by Mazhar Javed Awan, Rafia Asad Khan, Haitham Nobanee, Awais Yasin, Syed Muhammad Anwar, Usman Naseem and Vishwa Pratap Singh

Electronics 2021, 10(10), 1215; https://doi.org/10.3390/electronics10101215 - 20 May 2021

Cited by 66 | Viewed by 12235

Abstract

In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies. Our proposed system uses the last search data of a user regarding movie category and references this to instruct the recommender engine, thereby making a list of predictions for top ratings. The proposed study used a model-based approach of matrix factorization, the ALS algorithm along with a collaborative filtering technique, which solved the cold start, sparse, and scalability problems. In particular, we performed experimental analysis and successfully obtained minimum root mean squared errors (oRMSEs) of 0.8959 to 0.97613, approximately. Moreover, our proposed movie recommendation system showed an accuracy of 97% and predicted the top 1000 ratings for movies. Full article

(This article belongs to the Special Issue Big Data Privacy-Preservation)

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Review

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21 pages, 3893 KiB

Open AccessReview

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

by Laraib Aslam Haafza, Mazhar Javed Awan, Adnan Abid, Awais Yasin, Haitham Nobanee and Muhammad Shoaib Farooq

Electronics 2021, 10(24), 3125; https://doi.org/10.3390/electronics10243125 - 16 Dec 2021

Cited by 25 | Viewed by 5834

Abstract

The COVID-19 pandemic has frightened people worldwide, and coronavirus has become the most commonly used phrase in recent years. Therefore, there is a need for a systematic literature review (SLR) related to Big Data applications in the COVID-19 pandemic crisis. The objective is to highlight recent technological advancements. Many studies emphasize the area of the COVID-19 pandemic crisis. Our study categorizes the many applications used to manage and control the pandemic. There is a very limited SLR prospective of COVID-19 with Big Data. Our SLR study picked five databases: Science direct, IEEE Xplore, Springer, ACM, and MDPI. Before the screening, following the recommendation, Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) were reported for 893 studies from 2019, 2020 and until September 2021. After screening, 60 studies met the inclusion criteria through COVID-19 data statistics, and Big Data analysis was used as the search string. Our research’s findings successfully dealt with COVID-19 healthcare with risk diagnosis, estimation or prevention, decision making, and drug Big Data applications problems. We believe that this review study will motivate the research community to perform expandable and transparent research against the pandemic crisis of COVID-19. Full article

(This article belongs to the Special Issue Big Data Privacy-Preservation)

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22 pages, 2511 KiB

Open AccessReview

Privacy Preservation Models for Third-Party Auditor over Cloud Computing: A Survey

by Abdul Razaque, Mohamed Ben Haj Frej, Bandar Alotaibi and Munif Alotaibi

Electronics 2021, 10(21), 2721; https://doi.org/10.3390/electronics10212721 - 8 Nov 2021

Cited by 17 | Viewed by 3326

Abstract

Cloud computing has become a prominent technology due to its important utility service; this service concentrates on outsourcing data to organizations and individual consumers. Cloud computing has considerably changed the manner in which individuals or organizations store, retrieve, and organize their personal information. Despite the manifest development in cloud computing, there are still some concerns regarding the level of security and issues related to adopting cloud computing that prevent users from fully trusting this useful technology. Hence, for the sake of reinforcing the trust between cloud clients (CC) and cloud service providers (CSP), as well as safeguarding the CC’s data in the cloud, several security paradigms of cloud computing based on a third-party auditor (TPA) have been introduced. The TPA, as a trusted party, is responsible for checking the integrity of the CC’s data and all the critical information associated with it. However, the TPA could become an adversary and could aim to deteriorate the privacy of the CC’s data by playing a malicious role. In this paper, we present the state of the art of cloud computing’s privacy-preserving models (PPM) based on a TPA. Three TPA factors of paramount significance are discussed: TPA involvement, security requirements, and security threats caused by vulnerabilities. Moreover, TPA’s privacy preserving models are comprehensively analyzed and categorized into different classes with an emphasis on their dynamicity. Finally, we discuss the limitations of the models and present our recommendations for their improvement. Full article

(This article belongs to the Special Issue Big Data Privacy-Preservation)

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