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Mathematics
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Mathematical Modeling for Parallel and Distributed Processing, 2nd Edition
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Mathematical Modeling for Parallel and Distributed Processing, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1250

Special Issue Editors

Dr. Tian-Yi Li

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Guest Editor
Department of Computer Science, Aalborg University, 9220 Aalborg, Denmark
Interests: spatiotemporal database; distributed optimization; big graph data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Yushuai Li

E-Mail Website
Guest Editor
Department of Informatics, University of Oslo, 0316 Oslo, Norway
Interests: distributed optimization; energy management; machine learning; digitial twin
Special Issues, Collections and Topics in MDPI journals
Dr. Peiyuan Guan

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Guest Editor
Department of Informatics, University of Oslo, 0316 Oslo, Norway
Interests: edge computing; real-time systems; task scheduling; deep learning; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Dr. Lingxiao Yang

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Guest Editor
School of Artificial Intelligence, Anhui University, Hefei 230093, China
Interests: deep reinforcement learning; energy management; distributed optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lu Chen

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Guest Editor
College of Computer Science, Zhejiang University, Hangzhou 310027, China
Interests: database; big data management; AI interaction with dB technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Parallel and distributed processing have become increasingly essential for solving computationally intensive tasks. With the exponential growth of data and the increasing availability of CPU cores, efficient parallel and distributed processing solutions have become more desirable. However, despite decades of development, some fundamental challenges still exist, such as the distributed knowledge discovery of large-scale data, peer-to-peer energy trading under complex system environments, the improvement of the services in intelligent transportation systems, parallel training in deep learning and intelligent network management and task scheduling. Mathematical models can help address these challenges through resource utilization optimization, data mining and analytics, energy consumption minimization, and the reduction of communication overheads. By incorporating powerful mathematical models into parallel and distributed processing, we can achieve better performances, optimize computation and communication between nodes, and overcome the fundamental challenges that exist in this field.

The main objective of this Special Issue is to showcase innovative research that combines parallel and distributed computing with powerful and smart mathematical methods. We welcome submissions that present the latest developments in distributed optimization, machine learning-based distributed network management and orchestration, algorithm design, and mathematical modeling, as well as their applications in big data processing, data usability, energy, transportation, aerospace, and 5G/6G. By highlighting the latest advances in these fields, we aim to generate new ideas and foster collaborations that can address the current challenges and drive further progress in parallel and distributed computing.

Dr. Tian-Yi Li
Dr. Yushuai Li
Dr. Peiyuan Guan
Dr. Lingxiao Yang
Prof. Dr. Lu 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. Mathematics 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 2600 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

  • algebraic digital techniques
  • distributed computing
  • parallel processing
  • algorithm design
  • mathematical modeling
  • optimization

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Published Papers (2 papers)

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Research

26 pages, 7621 KiB  
Article
A Parallel Multi-Party Privacy-Preserving Record Linkage Method Based on a Consortium Blockchain
by Shumin Han, Zikang Wang, Dengrong Shen and Chuang Wang
Mathematics 2024, 12(12), 1854; https://doi.org/10.3390/math12121854 - 14 Jun 2024
Viewed by 374
Abstract
Privacy-preserving record linkage (PPRL) is the process of linking records from various data sources, ensuring that matching records for the same entity are shared among parties while not disclosing other sensitive data. However, most existing PPRL approaches currently rely on third parties for [...] Read more.
Privacy-preserving record linkage (PPRL) is the process of linking records from various data sources, ensuring that matching records for the same entity are shared among parties while not disclosing other sensitive data. However, most existing PPRL approaches currently rely on third parties for linking, posing risks of malicious tampering and privacy breaches, making it difficult to ensure the security of the linkage. Therefore, we propose a parallel multi-party PPRL method based on consortium blockchain technology which can effectively address the issue of semi-trusted third-party validation, auditing all parties involved in the PPRL process for potential malicious tampering or attacks. To improve the efficiency and security of consensus within a consortium blockchain, we propose a practical Byzantine fault tolerance consensus algorithm based on matching efficiency. Additionally, we have incorporated homomorphic encryption into Bloom filter encoding to enhance its security. To optimize computational efficiency, we have adopted the MapReduce model for parallel encryption and utilized a binary storage tree as the data structure for similarity computation. The experimental results show that our method can effectively ensure data security while also exhibiting relatively high linkage quality and scalability. Full article
(This article belongs to the Special Issue Mathematical Modeling for Parallel and Distributed Processing, 2nd Edition)
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20 pages, 5253 KiB  
Article
A Multi-Party Privacy-Preserving Record Linkage Method Based on Secondary Encoding
by Shumin Han, Yizi Wang, Derong Shen and Chuang Wang
Mathematics 2024, 12(12), 1800; https://doi.org/10.3390/math12121800 - 9 Jun 2024
Viewed by 623
Abstract
With the advent of the big data era, data security and sharing have become the core elements of new-era data processing. Privacy-preserving record linkage (PPRL), as a method capable of accurately and securely matching and sharing the same entity across multiple data sources, [...] Read more.
With the advent of the big data era, data security and sharing have become the core elements of new-era data processing. Privacy-preserving record linkage (PPRL), as a method capable of accurately and securely matching and sharing the same entity across multiple data sources, is receiving increasing attention. Among the existing research methods, although PPRL methods based on Bloom Filter encoding excel in computational efficiency, they are susceptible to privacy attacks, and the security risks they face cannot be ignored. To balance the contradiction between security and computational efficiency, we propose a multi-party PPRL method based on secondary encoding. This method, based on Bloom Filter encoding, generates secondary encoding according to well-designed encoding rules and utilizes the proposed linking rules for secure matching. Owing to its excellent encoding and linking rules, this method successfully addresses the balance between security and computational efficiency. The experimental results clearly show that, in comparison to the original Bloom Filter encoding, this method has nearly equivalent computational efficiency and linkage quality. The proposed rules can effectively prevent the re-identification problem in Bloom Filter encoding (proven). Compared to existing privacy-preserving record linkage methods, this method shows higher security, making it more suitable for various practical application scenarios. The introduction of this method is of great significance for promoting the widespread application of privacy-preserving record linkage technology. Full article
(This article belongs to the Special Issue Mathematical Modeling for Parallel and Distributed Processing, 2nd Edition)
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