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Electronics
Special Issues
Security and Privacy Preservation in Big Data Age
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Security and Privacy Preservation in Big Data Age

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

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 3676

Special Issue Editors

Prof. Dr. Hu Xiong

E-Mail Website
Guest Editor
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: cryptography; privacy-preserving computation; password security
Dr. Yanan Chen

E-Mail Website
Guest Editor
Teaching Department of Basic Courses, Jiangxi University of Science and Technology, Nanchang 330013, China
Interests: public-key cryptography; privacy computing; blockchain

Special Issue Information

Dear Colleagues,

Big data has an important and far-reaching impact on social development, scientific research, thinking modes, personal life and other areas. Naturally, individuals could enjoy increasing convenience due to big data. However, the era of big data also brings serious challenges for enterprises and individuals in cases where the security threats have not been properly treated. Thus, research on security and privacy protection issues in the big data age has attracted increasing interest from the academia and industry communities.

This Special Issue aims to address security and privacy protection issues in the big data age. The scope of this section includes, but is not limited to, these areas;

1. Artificial intelligence (AI) security algorithms: including cryptographic algorithms, information hiding and detection algorithms, digital watermark embedding and detection algorithms, image tampering authentication algorithms, privacy data mining and filtering algorithms, quantum cryptography algorithms, etc.

2. Crypto-security protocols: including authentication protocols, key exchange and management protocols, electronic payment protocols, security analysis theories and methods of security protocols, etc.

3. System security: including trusted computing, security architecture, distributed system security, database security, and information system security evaluation theories and methods.

4. Network security: including information confrontation, network attack and defense, security testing, intrusion detection, network survivability, vehicle networking security, security management, etc.

5. Cloud computing and Internet of Things security: cloud computing/Internet of Things security model, cloud computing/Internet of Things security needs and strategies, cloud computing/Internet of Things user privacy protection, cloud computing/Internet of Things infrastructure security protection.

6. Big data security: user privacy protection, privacy clustering algorithm, big data credibility, and big data access control in big data.

7. Security assessment: big data service security capability assessment, information system security assessment theory and method, privacy security assessment, etc.

  • Information system security and management;
  • Network security;
  • Information security algorithms and protocols;
  • User privacy protection in data;
  • Privacy data mining;
  • Network privacy management.

Prof. Dr. Hu Xiong
Dr. Yanan Chen
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

  • information security
  • system security
  • privacy security assessment
  • privacy clustering algorithm
  • cryptographic algorithms
  • the electromagnetic spectrum
  • antennas and propagation

Published Papers (3 papers)

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Research

15 pages, 2487 KiB  
Article
Attribute-Based Encryption Scheme with k-Out-of-n Oblivious Transfer
by Hao Zhang, Yue Zhao, Jintao Meng, Xue Wang and Kaijun Wu
Electronics 2023, 12(21), 4502; https://doi.org/10.3390/electronics12214502 - 01 Nov 2023
Viewed by 740
Abstract
Attribute-based encryption enables users to flexibly exchange and share files with others. In these schemes, users utilize their own attributes to acquire public-private key pairs from the key generation center. However, achieving this for users who wish to keep their attributes private poses [...] Read more.
Attribute-based encryption enables users to flexibly exchange and share files with others. In these schemes, users utilize their own attributes to acquire public-private key pairs from the key generation center. However, achieving this for users who wish to keep their attributes private poses a challenge. To address this contradiction, we propose an original scheme that combines ciphertext policy attribute-based encryption with a k-out-of-n oblivious transfer protocol. This scheme allows the distribution of corresponding public-private key pairs to users without the key generation center needing to obtain specific user attributes. Furthermore, it ensures the privacy of the key generation center. Security analysis demonstrates that the scheme is secure in the random oracle model. Our performance comparison and experimental results indicate that the scheme is both flexible and efficient. Full article
(This article belongs to the Special Issue Security and Privacy Preservation in Big Data Age)
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15 pages, 5027 KiB  
Article
Consideration of FedProx in Privacy Protection
by Tianbo An, Leyu Ma, Wei Wang, Yunfan Yang, Jingrui Wang and Yueren Chen
Electronics 2023, 12(20), 4364; https://doi.org/10.3390/electronics12204364 - 20 Oct 2023
Viewed by 963
Abstract
As federated learning continues to increase in scale, the impact caused by device and data heterogeneity is becoming more severe. FedProx, as a comparison algorithm, is widely used as a solution to deal with system heterogeneity and statistical heterogeneity in several scenarios. However, [...] Read more.
As federated learning continues to increase in scale, the impact caused by device and data heterogeneity is becoming more severe. FedProx, as a comparison algorithm, is widely used as a solution to deal with system heterogeneity and statistical heterogeneity in several scenarios. However, there is no work that comprehensively investigates the enhancements that FedProx can bring to current secure federation algorithms in terms of privacy protection. In this paper, we combine differential privacy and personalized differential privacy with FedProx, propose the DP-Prox and PDP-Prox algorithms under different privacy budget settings and simulate the algorithms on multiple datasets. The experiments show that the proposed algorithms not only significantly improve the convergence of the privacy algorithms under different heterogeneity conditions, but also achieve similar or even better accuracy than the baseline algorithm. Full article
(This article belongs to the Special Issue Security and Privacy Preservation in Big Data Age)
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17 pages, 3472 KiB  
Article
Secure Medical Data Collection in the Internet of Medical Things Based on Local Differential Privacy
by Jinpeng Wang and Xiaohui Li
Electronics 2023, 12(2), 307; https://doi.org/10.3390/electronics12020307 - 06 Jan 2023
Cited by 4 | Viewed by 1141
Abstract
As big data and data mining technology advance, research on the collection and analysis of medical data on the internet of medical things (IoMT) has gained increasing attention. Medical institutions often collect users’ signs and symptoms from their devices for analysis. However, the [...] Read more.
As big data and data mining technology advance, research on the collection and analysis of medical data on the internet of medical things (IoMT) has gained increasing attention. Medical institutions often collect users’ signs and symptoms from their devices for analysis. However, the process of data collection may pose a risk of privacy leakage without a trusted third party. To address this issue, we propose a medical data collection based on local differential privacy and Count Sketch (MDLDP). The algorithm first uses a random sampling technique to select only one symptom for perturbation by a single user. The perturbed data is then uploaded using Count Sketch. The third-party aggregates the user-submitted data to estimate the frequencies of the symptoms and the mean extent of their occurrence. This paper theoretically demonstrates that the designed algorithm satisfies local differential privacy and unbiased estimation. We also evaluated the algorithm experimentally with existing algorithms on a real medical dataset. The results show that the MDLDP algorithm has good utility for key-value type medical data collection statistics in the IoMT. Full article
(This article belongs to the Special Issue Security and Privacy Preservation in Big Data Age)
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