Blockchain and Big Data Analytics: AI-Driven Data Science

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Databases and Data Structures".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1211

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Special Issue Information

Dear Colleagues,

The integration of blockchain technology and big data analytics is transforming the landscape of data science and artificial intelligence (AI). Furthermore, as the volume of data continues to grow exponentially, ensuring integrity, transparency, and efficiency in data-driven decision-making is becoming a critical challenge. This Special Issue of Algorithms focuses on novel AI-driven approaches to leverage blockchain for secure, scalable, and decentralized data processing.

This Issue aims to provide a comprehensive view of how AI-driven data science can enhance the synergy between blockchain and big data analytics, fostering innovation in algorithm development and real-world applications.

We invite researchers to submit original contributions on cutting-edge methodologies that combine machine learning, deep learning, and optimization algorithms with blockchain technology for advanced big data analytics. Topics of interest include but are not limited to the following:

  • Blockchain-based data sharing, privacy, and security;
  • AI-enhanced decentralized machine learning (e.g., federated learning, edge AI);
  • Smart contracts for automated data processing and decision-making;
  • Consensus algorithms optimized for big data applications;
  • Cryptographic techniques for secure AI model training;
  • Real-world applications in healthcare, finance, the IoT, and beyond.

Prof. Dr. Sergey Y. Yurish
Guest Editor

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Keywords

  • blockchain-based data sharing, privacy, and security
  • AI-enhanced decentralized machine learning (e.g., federated learning, edge AI)
  • smart contracts for automated data processing and decision-making
  • consensus algorithms optimized for big data applications
  • cryptographic techniques for secure AI model training
  • real-world applications in healthcare, finance, the IoT, and beyond

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Published Papers (1 paper)

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Review

68 pages, 2780 KiB  
Review
AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
by Fujiang Yuan, Zihao Zuo, Yang Jiang, Wenzhou Shu, Zhen Tian, Chenxi Ye, Junye Yang, Zebing Mao, Xia Huang, Shaojie Gu and Yanhong Peng
Algorithms 2025, 18(5), 263; https://doi.org/10.3390/a18050263 - 2 May 2025
Viewed by 463
Abstract
With the continuous development of technology, blockchain has been widely used in various fields by virtue of its decentralization, data integrity, traceability, and anonymity. However, blockchain still faces many challenges, such as scalability and security issues. Artificial intelligence, with its powerful data processing [...] Read more.
With the continuous development of technology, blockchain has been widely used in various fields by virtue of its decentralization, data integrity, traceability, and anonymity. However, blockchain still faces many challenges, such as scalability and security issues. Artificial intelligence, with its powerful data processing capability, pattern recognition ability, and adaptive optimization algorithms, can improve the transaction processing efficiency of blockchain, enhance the security mechanism, and optimize the privacy protection strategy, thus effectively alleviating the limitations of blockchain in terms of scalability and security. Most of the existing related reviews explore the application of AI in blockchain as a whole but lack in-depth classification and discussion on how AI can empower the core aspects of blockchain. This paper explores the application of artificial intelligence technologies in addressing core challenges of blockchain systems, specifically in terms of scalability, security, and privacy protection. Instead of claiming a deep theoretical integration, we focus on how AI methods, such as machine learning and deep learning, have been effectively adopted to optimize blockchain consensus algorithms, improve smart contract vulnerability detection, and enhance privacy-preserving mechanisms like federated learning and differential privacy. Through comprehensive classification and discussion, this paper provides a structured overview of the current research landscape and identifies potential directions for further technical collaboration between AI and blockchain technologies. Full article
(This article belongs to the Special Issue Blockchain and Big Data Analytics: AI-Driven Data Science)
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