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Applied Sciences
Special Issues
New Challenges of Security and Privacy in Big Data Environment
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New Challenges of Security and Privacy in Big Data Environment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 January 2024) | Viewed by 2373

Special Issue Editor

Dr. Yajian Zhou

E-Mail Website
Guest Editor
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: text classification; ciphertext retrieval; big data analytics; data privacy protection

Special Issue Information

Dear Colleagues,

With the rapid development of information technology, data is exploding and the problem of privacy leakage is becoming increasingly serious. Nowadays, the study of privacy protection in the big data environment has become a hot topic. Big data is characterized by a large volume of data, a wide variety of data types, dynamic sources, fast data generation, etc. It is difficult to apply traditional privacy protection techniques apply in the big data environment, forcing the issue of privacy protection in the big data environment to confront new challenges.

This Special Issue focuses on the latest technologies for privacy protection in the Big Data environment, discussing the challenges and opportunities for privacy protection in the Big Data environment. We invite contributions on any recent advances in research in the field of Big Data privacy protection.

Kind regards,

Dr. Yajian 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. Applied Sciences 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 analytics
  • data privacy protection
  • text classification
  • information security
  • data access
  • cybersecurity

Published Papers (2 papers)

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Research

20 pages, 1489 KiB  
Article
An Improved Federated Learning-Assisted Data Aggregation Scheme for Smart Grids
by Bo Pang, Hui-Hui Liang, Ling-Hao Zhang, Yu-Fei Teng, Zheng-Wei Chang, Ze-Wei Liu, Chun-Qiang Hu and Wen-Hao Mou
Appl. Sci. 2023, 13(17), 9813; https://doi.org/10.3390/app13179813 - 30 Aug 2023
Viewed by 809
Abstract
In the context of rapid advancements in artificial intelligence (AI) technology, new technologies, such as federated learning and edge computing, have been widely applied in the power Internet of Things (PIoT). When compared to the traditional centralized training approach, conventional federated learning (FL) [...] Read more.
In the context of rapid advancements in artificial intelligence (AI) technology, new technologies, such as federated learning and edge computing, have been widely applied in the power Internet of Things (PIoT). When compared to the traditional centralized training approach, conventional federated learning (FL) significantly enhances privacy protection. Nonetheless, the approach poses privacy concerns, such as inferring other users’ training data through the global model or user-transferred parameters. In light of these challenges, this research paper introduces a novel privacy-preserving data aggregation scheme for the smart grid, bolstered by an improved FL technique. The secure multi-party computation (SMC) and differential privacy (DP) are skillfully combined with FL to combat inference attacks during both the learning process and output inference stages, thus furnishing robust privacy assurances. Through this approach, a trusted third party can securely acquire model parameters from power data holders and securely update the global model in an aggregated way. Moreover, the proposed secure aggregation scheme, as demonstrated through security analysis, achieves secure and reliable data aggregation in the electric PIoT environment. Finally, the experimental analysis shows that the proposed scheme effectively performs federated learning tasks, achieving good model accuracy and shorter execution times. Full article
(This article belongs to the Special Issue New Challenges of Security and Privacy in Big Data Environment)
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19 pages, 582 KiB  
Article
LTAnomaly: A Transformer Variant for Syslog Anomaly Detection Based on Multi-Scale Representation and Long Sequence Capture
by Delong Han, Mengjie Sun, Min Li and Qinghui Chen
Appl. Sci. 2023, 13(13), 7668; https://doi.org/10.3390/app13137668 - 28 Jun 2023
Viewed by 1181
Abstract
Detailed information on system operation is recorded by system logs, from which fast and accurate detection of anomalies is conducive to service management and system maintenance. Log anomaly detection methods often only handle a single type of anomaly, and the utilization of log [...] Read more.
Detailed information on system operation is recorded by system logs, from which fast and accurate detection of anomalies is conducive to service management and system maintenance. Log anomaly detection methods often only handle a single type of anomaly, and the utilization of log messages could be higher, which makes it challenging to improve the performance of log anomaly detection models. This article presents the LTAnomaly model to accomplish log anomaly detection using semantic information, sequence relationships, and component values to make a vector representation of logs, and we add Transformer with long short-term memory (LSTM) as our final classification model. When sequences are processed sequentially, the model is also influenced by the information from the global information, thus increasing the dependence on feature information. This improves the utilization of log messages with a flexible, simple, and robust model. To evaluate the effectiveness of our method, experiments are performed on the HDFS and BGL datasets, with the F1-measures reaching 0.985 and 0.975, respectively, showing that the proposed method enjoys higher accuracy and a more comprehensive application range than existing models. Full article
(This article belongs to the Special Issue New Challenges of Security and Privacy in Big Data Environment)
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