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Electronics
Special Issues
Security and Privacy Issues and Challenges in Big Data Era
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Security and Privacy Issues and Challenges in Big Data Era

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (5 August 2024) | Viewed by 8897

Special Issue Editors

Dr. Abdul Majeed

E-Mail Website
Guest Editor
Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea
Interests: privacy-preserving data publishing; differential privacy; synthetic data; machine learning; statistical disclosure control; low-cost anonymization methods; data-centric AI; federated learning
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaohan Zhang

E-Mail Website
Guest Editor
School of Law, Zhejiang University City College, Hangzhou 310015, China
Interests: privacy protection; digital economy; COVID-19; electronic commerce; digital health; privacy laws and regulations; law and technology; policy making; data analytics

Special Issue Information

Dear Colleagues,

Due to the recent proliferation in digital solutions such as epidemic handling systems (EHSs), social networks (SNs), recommender systems, cyber-physical social systems (CPSSs), and the internet of things (IoT), a large amount of personal data is being collected and processed. These collected data often contain information about an individual’s identity (i.e., demographics), spatial-temporal activities, salary and disease information, social life activities, etc. On the one hand, these data are regarded as the oil of the economy because they can influence science and advance societies when processed with advanced data mining and analytics tools. On the other hand, the mishandling of these data can spark public criticism and anger if they are not processed with tight privacy protection. The COVID-19 pandemic has also shown that privacy and security are two major bottlenecks when it comes to handling personal data encompassing basic and sensitive information. Furthermore, making sense of data (e.g., drawing conclusions out of data) with privacy preservation is another longstanding challenge in academia and research. To strike the balance between utility and privacy, many studies have been proposed. Nevertheless, some technical challenges and open research gaps remain in the area of privacy-preserving computing for analytics and mining purposes that leverage big data. 

This Special Issue aims to present recent advances in tools, methods, techniques, prototypes, case studies, and technologies to improve privacy and security by leveraging traditional and AI technologies in the big data era. Topics of interest include, but are not limited to: 

  • Privacy-preserving big data computing and processing;
  • Privacy-preserving data publishing;
  • Privacy-preserving data mining;
  • Anonymization of big data;
  • Differential privacy-based method to secure big data;
  • Social network privacy preservation;
  • Analytic techniques with privacy guarantees;
  • Emerging privacy threats due to the adoption of social networks; 
  • IoT privacy challenges and innovative solutions;
  • Big data privacy and security;
  • Cloud computing privacy issues and solutions;
  • Encryption techniques to protect the contents of big data;
  • Advance privacy protection techniques pertinent to the COVID era;
  • Privacy issues in cyber-physical social systems (CPSSs);
  • Case studies about people's perceptions of privacy in different regions;
  • Emerging privacy issues due to digitization across the globe;
  • Data-centric anonymization techniques to secure data sharing;
  • Light-weight anonymization methods for resource-constrained IoT environments;
  • Legal measures for privacy preservation in contact tracing methods;
  • Privacy protection techniques for heterogeneous data formats;
  • The data challenges posed by artificial intelligence in societal domains;
  • Privacy preservation of AI-based systems such as federated learning;
  • Privacy-enhancing techniques for big data-based smart healthcare applications;
  • Privacy protection for heterogeneous data styles (images, text, tables, multimedia, transactional databases, trajectories, etc.).

Dr. Abdul Majeed
Dr. Xiaohan Zhang
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. 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
  • anonymization
  • differential privacy
  • privacy and security
  • statistical disclosure control
  • IoT
  • COVID-19
  • cybersecurity in big data
  • data generalization
  • encryption
  • privacy-aware big data analytics
  • privacy aspects of big data in smart healthcare
  • medical applications
  • privacy protection in the lifecycle of AI applications
  • de-anonymization

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Published Papers (3 papers)

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Research

24 pages, 4153 KiB  
Article
Introducing the UWF-ZeekDataFall22 Dataset to Classify Attack Tactics from Zeek Conn Logs Using Spark’s Machine Learning in a Big Data Framework
by Sikha S. Bagui, Dustin Mink, Subhash C. Bagui, Pooja Madhyala, Neha Uppal, Tom McElroy, Russell Plenkers, Marshall Elam and Swathi Prayaga
Electronics 2023, 12(24), 5039; https://doi.org/10.3390/electronics12245039 - 18 Dec 2023
Cited by 2 | Viewed by 2243
Abstract
This study introduces UWF-ZeekDataFall22, a newly created dataset labeled using the MITRE ATT&CK framework. Although the focus of this research is on classifying the never-before classified resource development tactic, the reconnaissance and discovery tactics were also classified. The results were also compared to [...] Read more.
This study introduces UWF-ZeekDataFall22, a newly created dataset labeled using the MITRE ATT&CK framework. Although the focus of this research is on classifying the never-before classified resource development tactic, the reconnaissance and discovery tactics were also classified. The results were also compared to a similarly created dataset, UWF-ZeekData22, created in 2022. Both of these datasets, UWF-ZeekDataFall22 and UWF-ZeekData22, created using Zeek Conn logs, were stored in a Big Data Framework, Hadoop. For machine learning classification, Apache Spark was used in the Big Data Framework. To summarize, the uniqueness of this work is its focus on classifying attack tactics. For UWF-ZeekdataFall22, the binary as well as the multinomial classifier results were compared, and overall, the results of the binary classifier were better than the multinomial classifier. In the binary classification, the tree-based classifiers performed better than the other classifiers, although the decision tree and random forest algorithms performed almost equally well in the multinomial classification too. Taking training time into consideration, decision trees can be considered the most efficient classifier. Full article
(This article belongs to the Special Issue Security and Privacy Issues and Challenges in Big Data Era)
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27 pages, 7780 KiB  
Article
A Novel Cloud Enabled Access Control Model for Preserving the Security and Privacy of Medical Big Data
by Abdullah Alabdulatif, Navod Neranjan Thilakarathne and Kassim Kalinaki
Electronics 2023, 12(12), 2646; https://doi.org/10.3390/electronics12122646 - 13 Jun 2023
Cited by 28 | Viewed by 3448
Abstract
In the context of healthcare, big data refers to a complex compilation of digital medical data collected from many sources that are difficult to manage with normal technology and software due to its size and complexity. These big data are useful in various [...] Read more.
In the context of healthcare, big data refers to a complex compilation of digital medical data collected from many sources that are difficult to manage with normal technology and software due to its size and complexity. These big data are useful in various aspects of healthcare, such as disease diagnosis, early prevention of diseases, and predicting epidemics. Even though medical big data has many advantages and a lot of potential for revolutionizing healthcare, it also has a lot of drawbacks and problems, of which security and privacy are of the utmost concern, owing to the severity of the complications once the medical data is compromised. On the other hand, it is evident that existing security and privacy safeguards in healthcare organizations are insufficient to protect their massive, big data repositories and ubiquitous environment. Thus, motivated by the synthesizing of the current knowledge pertaining to the security and privacy of medical big data, including the countermeasures, in the study, firstly, we provide a comprehensive review of the security and privacy of medical big data, including countermeasures. Secondly, we propose a novel cloud-enabled hybrid access control framework for securing the medical big data in healthcare organizations, and the result of this research indicates that the proposed access control model can withstand most cyber-attacks, and it is also proven that the proposed framework can be utilized as a primary base to build secure and safe medical big data solutions. Thus, we believe this research would be useful for future researchers to comprehend the knowledge on the security and privacy of medical big data and the development of countermeasures. Full article
(This article belongs to the Special Issue Security and Privacy Issues and Challenges in Big Data Era)
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32 pages, 837 KiB  
Article
A Generic Approach towards Enhancing Utility and Privacy in Person-Specific Data Publishing Based on Attribute Usefulness and Uncertainty
by Abdul Majeed and Seong Oun Hwang
Electronics 2023, 12(9), 1978; https://doi.org/10.3390/electronics12091978 - 24 Apr 2023
Cited by 1 | Viewed by 2005
Abstract
This paper proposes a generic anonymization approach for person-specific data, which retains more information for data mining and analytical purposes while providing considerable privacy. The proposed approach takes into account the usefulness and uncertainty of attributes while anonymizing the data to significantly enhance [...] Read more.
This paper proposes a generic anonymization approach for person-specific data, which retains more information for data mining and analytical purposes while providing considerable privacy. The proposed approach takes into account the usefulness and uncertainty of attributes while anonymizing the data to significantly enhance data utility. We devised a method for determining the usefulness weight for each attribute item in a dataset, rather than manually deciding (or assuming based on domain knowledge) that a certain attribute might be more useful than another. We employed an information theory concept for measuring the uncertainty regarding sensitive attribute’s value in equivalence classes to prevent unnecessary generalization of data. A flexible generalization scheme that simultaneously considers both attribute usefulness and uncertainty is suggested to anonymize person-specific data. The proposed methodology involves six steps: primitive analysis of the dataset, such as analyzing attribute availability in the data, arranging the attributes into relevant categories, and sophisticated pre-processing, computing usefulness weights of attributes, ranking users based on similarities, computing uncertainty in sensitive attributes (SAs), and flexible data generalization. Our methodology offers the advantage of retaining higher truthfulness in data without losing guarantees of privacy. Experimental analysis on two real-life benchmark datasets with varying scales, and comparisons with prior state-of-the-art methods, demonstrate the potency of our anonymization approach. Specifically, our approach yielded better performance on three metrics, namely accuracy, information loss, and disclosure risk. The accuracy and information loss were improved by restraining heavier anonymization of data, and disclosure risk was improved by preserving higher uncertainty in the SA column. Lastly, our approach is generic and can be applied to any real-world person-specific tabular datasets encompassing both demographics and SAs of individuals. Full article
(This article belongs to the Special Issue Security and Privacy Issues and Challenges in Big Data Era)
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