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Symmetry
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Symmetry Applied in Privacy and Security for Big Data Analytics
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Symmetry Applied in Privacy and Security for Big Data Analytics

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 6629

Special Issue Editor

Prof. Dr. Pan Zhou

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: big data privacy and security; artificial intelligence security; IoT security and computing; online learning; deep learning; industrail IOT; computer vision and its security; wireless network security, reinforcement learning and other cutting-edge artificial intelligence design and privacy protection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

With the rapid development of information and communication technologies (ICTs; e.g., 5G, mobile edge computing, smart mobile edge devices, and social networks), the generated massive big datasets lead to an exceptional increase in research activities in big data. At present, many big data analytics tasks request the reform of mobile users’ personal data protection legal framework in the context of the adoption of a General Data Protection Regulation (GDPR), which exhibits the importance of privacy and security issues in big data research. Big data security has become an indispensable requirement in our lives—not only for personal privacy, but also for exploring and the potential symmetry to assure analytics efficiency and security performance. Advanced associated technologies central to and at the periphery of this research domain include blockchain, mobile cloud/edge computing, artificial intelligence, social networks, smart healthcare, smart city, Industry 4.0, etc. For example, blockchain has the capability to enhance predictive analytics because it verifies data validity, preventing false information with trustness from being included in big data analyses. The big data security market was valued at USD 17.38 billion in 2019, and is projected to reach USD 57.29 billion by 2027, growing at a CAGR of 17.35% from 2020 to 2027. This Special Issue aims to present cutting-edge research addressing privacy and security protection challenges in big data analytics. Original and unpublished high-quality research results are solicited to explore various challenging topics which include, but are not limited to: 

  • The intersection of blockchain and the symmetry applied in privacy and security issues of big data analytics;
  • Symmetry applied in the privacy and security of personal health records big data;
  • Security/privacy/trust and symmetry applied in big data issues in mobile cloud/edge computing;
  • Security/privacy/trust and symmetry applied in big data issues in social networks, smart healthcare, smart cities, Industry 4.0, etc.;
  • Security/privacy/trust-enabled big data mining methods for symmetry applied in big data analytics;
  • Adversarial attack and defense in symmetry-applied AI-enabled big data systems;
  • Symmetry applied in case study experience reports on big data security, privacy, and trust;
  • Symmetry applied in private information retrieval over typical big data platforms;
  • Symmetry applied in data-centric security and data classification;
  • Symmetry applied in cost and usability models related security issues in mobile and social network big data;
  • Symmetry applied in privacy-preserving machine-learning methods for big data analytics;
  • Symmetry applied in security and privacy policies for big data analytics;
  • Symmetry applied in secure big data management. 

Prof. Dr. Pan Zhou
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. Symmetry 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 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 analytics
  • privacy and security for big data
  • blockchain
  • social networks
  • health records
  • mobile cloud
  • smart cities

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Published Papers (3 papers)

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Research

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15 pages, 3720 KiB  
Article
An Efficient and Universal Real-Time Data Integrity Verification Scheme Based on Symmetric Key in Stream Computing System
by Hongyuan Wang, Wanting Zhu, Baokai Zu and Yafang Li
Symmetry 2023, 15(8), 1541; https://doi.org/10.3390/sym15081541 - 4 Aug 2023
Cited by 1 | Viewed by 1424
Abstract
The integrity of real-time data streams has not been solved for a long time and has gradually become a difficult problem in the field of data security. Most of the current data integrity verification schemes are constructed using cryptographic algorithms with complex computation, [...] Read more.
The integrity of real-time data streams has not been solved for a long time and has gradually become a difficult problem in the field of data security. Most of the current data integrity verification schemes are constructed using cryptographic algorithms with complex computation, which cannot be directly applied to real-time stream computing systems. Aiming at the above issue, this paper adopts the Carter–Wegman MAC method, pseudo-random function and symmetric cryptography mechanism to construct the Real-Time Data Integrity Verification scheme based on symmetric key in stream computing systems (RT-DIV), which converts a one-time MAC to a multiple-time MAC and retains the advantage of security performance. Then, a security analysis is given under the standard model. Finally, experiments and data analysis are conducted in a simulated environment, and the experimental results show that the RT-DIV scheme can effectively guarantee the integrity of real-time data streams. Furthermore, the RT-DIV scheme lays the foundation for the secure application of the stream computing system. Full article
(This article belongs to the Special Issue Symmetry Applied in Privacy and Security for Big Data Analytics)
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21 pages, 2335 KiB  
Article
Malware Detection Using Deep Learning and Correlation-Based Feature Selection
by Esraa Saleh Alomari, Riyadh Rahef Nuiaa, Zaid Abdi Alkareem Alyasseri, Husam Jasim Mohammed, Nor Samsiah Sani, Mohd Isrul Esa and Bashaer Abbuod Musawi
Symmetry 2023, 15(1), 123; https://doi.org/10.3390/sym15010123 - 1 Jan 2023
Cited by 101 | Viewed by 11208
Abstract
Malware is one of the most frequent cyberattacks, with its prevalence growing daily across the network. Malware traffic is always asymmetrical compared to benign traffic, which is always symmetrical. Fortunately, there are many artificial intelligence techniques that can be used to detect malware [...] Read more.
Malware is one of the most frequent cyberattacks, with its prevalence growing daily across the network. Malware traffic is always asymmetrical compared to benign traffic, which is always symmetrical. Fortunately, there are many artificial intelligence techniques that can be used to detect malware and distinguish it from normal activities. However, the problem of dealing with large and high-dimensional data has not been addressed enough. In this paper, a high-performance malware detection system using deep learning and feature selection methodologies is introduced. Two different malware datasets are used to detect malware and differentiate it from benign activities. The datasets are preprocessed, and then correlation-based feature selection is applied to produce different feature-selected datasets. The dense and LSTM-based deep learning models are then trained using these different versions of feature-selected datasets. The trained models are then evaluated using many performance metrics (accuracy, precision, recall, and F1-score). The results indicate that some feature-selected scenarios preserve almost the same original dataset performance. The different nature of the used datasets shows different levels of performance changes. For the first dataset, the feature reduction ratios range from 18.18% to 42.42%, with performance degradation of 0.07% to 5.84%, respectively. The second dataset reduction rate is between 81.77% and 93.5%, with performance degradation of 3.79% and 9.44%, respectively. Full article
(This article belongs to the Special Issue Symmetry Applied in Privacy and Security for Big Data Analytics)
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Review

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22 pages, 2974 KiB  
Review
Analysis of Blockchain in the Healthcare Sector: Application and Issues
by Ammar Odeh, Ismail Keshta and Qasem Abu Al-Haija
Symmetry 2022, 14(9), 1760; https://doi.org/10.3390/sym14091760 - 23 Aug 2022
Cited by 68 | Viewed by 7132
Abstract
The emergence of blockchain technology makes it possible to address disparate distributed system security concerns in formerly ridiculous practices. A key factor of this ability is the decentralization of the symmetrically distributed ledgers of blockchain. Such decentralization has replaced several security functionalities of [...] Read more.
The emergence of blockchain technology makes it possible to address disparate distributed system security concerns in formerly ridiculous practices. A key factor of this ability is the decentralization of the symmetrically distributed ledgers of blockchain. Such decentralization has replaced several security functionalities of centralized authority with the use of cryptographic systems. That is, public or asymmetric cryptography is the key part of what makes blockchain technology possible. Recently, the blockchain experience introduces the chance for the healthcare field to implement these knowhows in their electronic records. This adoption supports retaining and sharing the symmetrical patient records with the appropriate alliance of hospitals and healthcare providers in a secure decentralized system, using asymmetric cryptography like hashing, digitally signed transactions, and public key infrastructure. These include specialized applications for drug tracking, applications for observing patients, or Electronic Health Records (EHR). Therefore, it is essential to notice that the principled awareness of the healthcare professionals is the leading point of the right perception ethics. In this work, we provide a thorough review of the issues and applications of utilizing blockchain in the healthcare and medical fields emphasizing the particular challenges and aspects. The study adopted a systematic review of secondary literature in answering the research question. Specifically, this paper aims to investigate how blockchain technology can be applied to improve the overall performance of the healthcare sector and to explore the various challenges and concerns of the application of blockchain in the healthcare system. Full article
(This article belongs to the Special Issue Symmetry Applied in Privacy and Security for Big Data Analytics)
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