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Algorithms
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Blockchain and Big Data Analytics: AI-Driven Data Science
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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: closed (31 December 2025) | Viewed by 17331

Editor

Prof. Dr. Sergey Y. Yurish

E-Mail Website
Guest Editor
International Frequency Sensor Association (IFSA), 08860 Castelldefels, Spain
Interests: smart sensors; intelligent sensors; frequency output optical sensors; frequency-to-digital converters; measurement of frequency
Special Issues, Collections and Topics in MDPI journals

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

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms 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 1800 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

  • 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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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25 pages, 5809 KB  
Article
Chainguard: A Blockchain-Based Aid Distribution System with Mobile Application and System Architecture Design
by Enes Rayman, Serra Öğütcen, Okan Yaman and Yusuf Murat Erten
Algorithms 2026, 19(5), 366; https://doi.org/10.3390/a19050366 - 5 May 2026
Viewed by 476
Abstract
Natural disasters are devastating occurrences that have a major influence on the well-being of numerous individuals on a global scale. The primary goal of this study is to facilitate the rapid, transparent, and safe delivery of various aid such as food and clothing [...] Read more.
Natural disasters are devastating occurrences that have a major influence on the well-being of numerous individuals on a global scale. The primary goal of this study is to facilitate the rapid, transparent, and safe delivery of various aid such as food and clothing to people in disaster areas. For this purpose, a system has been established using blockchain technology in cooperation with institutions and humanitarian organizations. This system is designed to be accountable and reliable; it will supervise all processes from the source of aid materials to their distribution while protecting the personal information of disaster victims. The assistance process is improved using Smart Contracts in order to provide fast, effective, and coordinated assistance. Unlike existing humanitarian frameworks that rely on permissionless networks such as Bitcoin or Ethereum, this study proposes Hyperledger Fabric to ensure beneficiary privacy and eliminate per-transaction fees for end-users, thereby offering a more sustainable economic model for high-frequency aid distribution compared to public blockchains. The proposed system (Chainguard) addresses the ’efficiency gap’ in the current literature JSON Web Token (JWT)-based authentication layer. The results showed that Chainguard achieves a stable throughput of ~180 TPS with an end-to-end latency of less than 1.5 s, outperforming traditional heavy-cryptography models in terms of scalability and resource efficiency during real-time disaster response. Full article
(This article belongs to the Special Issue Blockchain and Big Data Analytics: AI-Driven Data Science)
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34 pages, 28662 KB  
Article
Template-Driven Multimodal Face Pseudonymization for Privacy-Preserving Big Data Analytics
by Yeong Su Lee, Hendrik Bothe and Michaela Geierhos
Algorithms 2026, 19(3), 176; https://doi.org/10.3390/a19030176 - 26 Feb 2026
Viewed by 799
Abstract
Profile images from social networks are a valuable source of data for AI analytics, but they contain biometric identifiers that pose serious privacy risks. The current face anonymization techniques often destroy semantic information, and generative de-identification methods are vulnerable to re-identification attacks. In [...] Read more.
Profile images from social networks are a valuable source of data for AI analytics, but they contain biometric identifiers that pose serious privacy risks. The current face anonymization techniques often destroy semantic information, and generative de-identification methods are vulnerable to re-identification attacks. In this paper, we propose a template-driven multimodal face pseudonymization framework that allows for the privacy-preserving analysis of facial image data while retaining analytically relevant attributes. Our approach uses a FaceNet-based CelebA attribute classifier to extract fine-grained facial attributes and a DeepFace model to extract high-level demographic attributes. Rather than relying on stochastic large language models, we introduce deterministic template-based attribute-to-text conversion to ensure consistency and reproducibility and prevent unintended attribute hallucination. The resulting textual description serves as the sole conditioning input for Janus-Pro, a multimodal text-to-image generation model that synthesizes realistic yet non-identifiable face images. We evaluate our method on the CelebA dataset under a strong adversarial threat model, employing state-of-the-art face recognition systems to assess re-identification and linkability attacks. Our results demonstrate a substantial reduction in identity leakage while preserving semantic attributes. Full article
(This article belongs to the Special Issue Blockchain and Big Data Analytics: AI-Driven Data Science)
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46 pages, 4638 KB  
Article
Blockchain-Native Asset Direction Prediction: A Confidence-Threshold Approach to Decentralized Financial Analytics Using Multi-Scale Feature Integration
by Oleksandr Kuznetsov, Dmytro Prokopovych-Tkachenko, Maksym Bilan, Borys Khruskov and Oleksandr Cherkaskyi
Algorithms 2025, 18(12), 758; https://doi.org/10.3390/a18120758 - 29 Nov 2025
Viewed by 2827
Abstract
Blockchain-based financial ecosystems generate unprecedented volumes of multi-temporal data streams requiring sophisticated analytical frameworks that leverage both on-chain transaction patterns and off-chain market microstructure dynamics. This study presents an empirical evaluation of a two-class confidence-threshold framework for cryptocurrency direction prediction, systematically integrating macro [...] Read more.
Blockchain-based financial ecosystems generate unprecedented volumes of multi-temporal data streams requiring sophisticated analytical frameworks that leverage both on-chain transaction patterns and off-chain market microstructure dynamics. This study presents an empirical evaluation of a two-class confidence-threshold framework for cryptocurrency direction prediction, systematically integrating macro momentum indicators with microstructure dynamics through unified feature engineering. Building on established selective classification principles, the framework separates directional prediction from execution decisions through confidence-based thresholds, enabling explicit optimization of precision–recall trade-offs for decentralized financial applications. Unlike traditional three-class approaches that simultaneously learn direction and execution timing, our framework uses post-hoc confidence thresholds to separate these decisions. This enables systematic optimization of the accuracy-coverage trade-off for blockchain-integrated trading systems. We conduct comprehensive experiments across 11 major cryptocurrency pairs representing diverse blockchain protocols, evaluating prediction horizons from 10 to 600 min, deadband thresholds from 2 to 20 basis points, and confidence levels of 0.6 and 0.8. The experimental design employs rigorous temporal validation with symbol-wise splitting to prevent data leakage while maintaining realistic conditions for blockchain-integrated trading systems. High confidence regimes achieve peak profits of 167.64 basis points per trade with directional accuracies of 82–95% on executed trades, suggesting potential applicability for automated decentralized finance (DeFi) protocols and smart contract-based trading strategies on similar liquid cryptocurrency pairs. The systematic parameter optimization reveals fundamental trade-offs between trading frequency and signal quality in blockchain financial ecosystems, with high confidence strategies reducing median coverage while substantially improving per-trade profitability suitable for gas-optimized on-chain execution. Full article
(This article belongs to the Special Issue Blockchain and Big Data Analytics: AI-Driven Data Science)
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Review

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68 pages, 2780 KB  
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
Cited by 32 | Viewed by 10720
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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Other

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18 pages, 4973 KB  
Project Report
Data Management and Data Services in Large Collaborative Projects—DiverSea Experience
by Vassil Vassilev, Georgi Petkov, Boris Kraychev, Stoyan Haydushki, Stoyan Nikolov, Viktor Sowinski-Mydlarz, Ensiye Kiyamousavi, Nikolay Shivarov and Denitsa Stoilova
Algorithms 2026, 19(2), 154; https://doi.org/10.3390/a19020154 - 15 Feb 2026
Viewed by 544
Abstract
Collaborative projects under the Horizon Europe Framework Program of the European Union typically involve a large number of partners from multiple countries. Data-centric projects, among them, often require integration of disparate data source formats and collection methods, leading to complex data management architectures [...] Read more.
Collaborative projects under the Horizon Europe Framework Program of the European Union typically involve a large number of partners from multiple countries. Data-centric projects, among them, often require integration of disparate data source formats and collection methods, leading to complex data management architectures and policies. This article is an extended version of an article presented at the 1st International Conference on Big Data Analytics and Applications (BDAA’2025). It explores design decisions, organisational principles, and technological solutions to address these challenges by focusing on data integration of data sources and the hybridisation of data services. This experience was gathered while working on DiverSea, a project dedicated to the analysis of biodiversity dynamics along European coastlines—ranging from the Black Sea to the Mediterranean and the North Sea. While grounded in established technologies, the project’s takeaways offer valuable insights for environmental data projects across aquatic, terrestrial, and atmospheric domains. Full article
(This article belongs to the Special Issue Blockchain and Big Data Analytics: AI-Driven Data Science)
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