Blockchain Papers

Mohamed Ezz, Alaa Alaerjan, Ayman Mohamed Mostafa

In today's rapidly advancing healthcare landscape, integrating Artificial Intelligence (AI) and Machine Learning (ML) has the potential to significantly improve patient care and streamline medical processes. The utilization of confidential patient data to train and develop these technologies, however, raises significant concerns regarding authenticity, security, and privacy. In this study, we introduce MediChainAI, a safe and practical framework that allows patients full ownership over their own health data by integrating Self-Sovereign Identity (SSI), Blockchain, and sophisticated cryptography techniques. By clearly outlining the goals and parameters of this access, MediChainAI allows patients to safely and selectively share data with healthcare providers and researchers. While SSI guarantees that patients have ownership of their data, the framework uses Blockchain technology to keep things transparent and secure. Further, MediChainAI makes use of Merkle trees, which provide verified access to subsets of data without jeopardizing the privacy of the whole dataset. The encryption mechanism, which is based on smart contracts, is a distinctive feature of the framework that allows researchers and medical practitioners controlled and secure access to patient data. In order to improve the accuracy and reliability of medical diagnoses and treatment, this strategy makes sure that only confirmed, legitimate data is utilized to train medical models. A significant step toward safer and more personalized healthcare, MediChainAI encourages ethical and patient-focused innovation by effectively resolving essential issues regarding data security and patient privacy.

K P Loganaadhan, Vijay V Sham, G R Abijith

Traditional health insurance models depend on fixed policy structures that often lack personalization and transparency. These static systems are slow to respond to individual health behaviors, which leads to inefficient premium calculations and rigid coverage terms. In this paper, we propose a decentralized and smart framework that allows real-time negotiation of health insurance coverage using dynamic non-fungible tokens (dNFTs) and AI-powered risk profiling. Our system uses Ethereum smart contracts to represent health insurance policies as dNFTs that change based on individual health metrics collected from wearable devices or electronic health records. A builtin machine learning engine evaluates user risk profiles in realtime, updating the dNFT metadata as needed. Insurers interact with the system through a decentralized marketplace, where they bid to provide personalized coverage terms based on the live health profile encoded in the token. The negotiation process is trustless, transparent, and automatic using smart contracts, which removes intermediaries and cuts down processing time. We show a working prototype deployed on the Ethereum testnet and assess it using simulated user data. The results indicate a noticeable improvement in policy adaptability, personalization, and claim settlement time. This approach has the potential to transform health insurance by making it dynamic, data-driven, and fully decentralized.

Akimoto, Hitoshi

The ethical tension surrounding AI-generated art often arises from misconceptions that anthropomorphize the algorithmic process. The accusation that “AI steals human creativity” overlooks the mediating role of human design and data literacy. This paper reframes the debate as a problem of informational asymmetry rather than morality. It proposes that Non-Fungible Tokens (NFTs) and Digital Object Identifiers (DOIs) can visualize and authenticate the flow of creative tension within a transparent ecosystem. NFTs serve as formal anchors—recording authorship, signature, and temporal origin—while DOIs preserve the conceptual framework and creative process. When linked, these two systems transform authorship into a traceable circulation of knowledge, allowing the boundary between plagiarism, homage, and originality to be objectively determined. This dual-layer provenance model presents an ethical infrastructure for creation in the age of generative AI.

Shafiq Ul Rehman, Kamil Reza Khondakar, Ameenuddin

No abstract is available for this record.

Yair Rivera Julio, Ángel D. Pinto Mangones, Nelson A. Pérez-García, Mónica-Karel Huerta · 9 authors

Large-Language-Model (LLM) functionality is rapidly becoming a cornerstone of Telemedicine-as-a-Service (PGaaS) platforms. Recent Q1 studies demonstrate that even minuscule training-set or parameter perturbations can introduce persistent back-doors, while inference pipelines leak protected health information (PHI) if left unguarded. Building on the NIST AI Risk Management Framework (AI RMF), this paper proposes and implements a zero-trust, multi-cloud security architecture that couples (i) knowledge-graph–driven data-integrity validation, (ii) containerised fine-tuning isolation, (iii) AI-RMF–centred governance and continuous risk registers, (iv) a privacy-preserving response-sanitisation gateway enhanced with one-time-password (OTP) and KYC identity binding, and (v) remote-attestation-backed zero-knowledge-proof (ZKP) integrity challenges for model weights at runtime. An extensive multi-cloud evaluation shows that the framework detects 94.6 % of tainted samples before ingestion and blocks 91.3 % of unsafe outputs, with a median latency overhead of 66 ms—well below clinical tele-consultation thresholds.

Sandeep Kalari, Ravi Mukkamala, Vikas Ashok, Stephan Olariu · 6 authors

Large Language Models (LLMs) are increasingly being adopted in sensitive domains such as healthcare and finance. However, persistent challenges such as unreliable data sources, privacy breaches, and hallucinated output continue to hinder their usage. To address these shortcomings, we propose BlockQwen, a blockchain-augmented framework that integrates decentralized trust validation, role-specific access control, and verifiable audit trails into Qwen 2.5 LLM workflow. Here, blockchain not only anchors the authenticity of retrieved documents, but also enforces dynamic, tamper-proof access and authorization policies and preserves transparent, immutable records of model interactions. This decentralized infrastructure ensures that the LLM operates on verified inputs while maintaining privacy and compliance with regulatory standards. A layered security module, combined with reinforcement learning, is further tailored to detect privacy risks and to mitigate hallucinations in real time. A prototype implementation, with simulated healthcare data, achieved an $86.25 \%$ privacy preservation rate and an $88.33 \%$ hallucination mitigation rate, significantly outperforming conventional LLM deployments. These results demonstrate the potential of combining blockchain functionality with LLM, resulting in robust, secure, transparent, and trustworthy AI systems.

Gianmarco Di Palma, Roberto Scendoni, Davide Ferorelli, Anna De Benedictis · 6 authors

Background/Objectives: Artificial intelligence (AI) is revolutionizing the healthcare industry, improving diagnoses, treatments, and clinical processes. However, its integration poses significant cybersecurity risks, including data breaches, algorithmic opacity, and vulnerabilities in AI-controlled medical devices. This narrative review analyzes these threats and evaluates blockchain technology as a potential mitigation strategy within a Clinical Risk Management framework. Methods: The literature search was conducted on PubMed, Scopus, and Web of Science, considering peer-reviewed publications from 2000 to January 2025. 1,204 articles were identified. Inclusion criteria included studies on cybersecurity risks in healthcare, blockchain applications in the clinical setting, and regulatory references (eg, General Data Protection Regulation). Conference abstracts, non-English articles, and non-peer-reviewed contributions were excluded. To ensure methodological rigor, the Scale for the Assessment of Narrative Review Articles criteria were applied. Results: The thematic analysis highlighted recurring critical issues: difficulties with informed consent, unauthorized access to sensitive data, and systemic vulnerabilities in hospital digital infrastructures. Blockchain presents a promising solution thanks to its decentralization, immutability, and transparency. Integration with smart contracts enables dynamic consent management, secure data sharing, and real-time monitoring of medical devices. Permissioned networks improve traceability and regulatory compliance, while Layer 2 solutions and optimized consent protocols address scalability challenges. Conclusion: Despite its potential, blockchain adoption faces obstacles: high costs, regulatory rigidity, and poor acceptance among healthcare professionals. The review highlights the need for pilot projects, interdisciplinary collaboration, and regulatory updates for effective integration. Combining AI and blockchain in Clinical Risk Management can transform clinical risk management from reactive to proactive, improving patient safety, data governance, and accountability.

Lilia Tightiz, Abdulkhamidov Akbarjon Sobitkhon Ughli, Joon Yoo

This paper presents a novel health data analysis platform for improved individualized risk forecasting of permanent pacemaker implantation (PPI) following transcatheter aortic valve replacement (TAVR) procedures. Specifically, we introduce XGBoost-federated adaptive interpolation transfer learning (XG-FedAIT)—a platform that integrates heterogeneous hospital data sets via adaptive output-level interpolation and performance-weighted model ensembling. This approach facilitates federated learning across institutions with different feature spaces and prediction targets, such as time-to-event and binary models, eliminating structural and semantic mismatches prevalent in regular federated transfer learning. To ensure secure, privacy-preserving, and general data protection regulation (GDPR)-compliant data exchange, we propose a dual-chain blockchain architecture integrating proof of authority (PoA), zero-knowledge proofs (ZKPs), and chain-specific smart contracts, and off-chain encrypted storage through Filecoin. Experimental evaluation proves that the PrimaryChain handles over 300 tx/s with a median latency of 500 ms, and the SecondaryChain offers 99.8% data availability and decentralized access control. The system is scalable to handle up to 2,500 transactions/hour, and the federated learning pipeline classifies PPI risk with an F1-score of 0.85 and AUROC of 0.91. These results support the effectiveness of our system in delivering real-time, regulation-compliant, and clinically actionable cardiac care in distributed environments.

Seid Abdu

We present a graphics processing unit (GPU)-accelerated Proof-of-Work (PoW) blockchain design tailored for secure healthcare data management. Our Compute Unified Device Architecture (CUDA)-optimized PoW achieves throughput improvements of approximately 5× to 100× and reduces block-formation latency compared to Central Processing Unit (CPU) mining, making blockchain practical for high-volume health records. We benchmark against standard platforms-Bitcoin, known for its robust security but slow block times; Ethereum (legacy PoW), widely adopted yet less efficient; and Hyperledger Fabric, a permissioned enterprise framework-to quantify performance gains. Empirical tests show GPU-Advanced Encryption Standard in Counter Mode (AES-CTR) processes large health-record payloads in under one second, while our PoW mining throughput improves by approximately 5×, to 100× relative to unaccelerated baselines. We also evaluate end-to-end encryption latency and discuss privacy trade-offs, including that lightweight Advanced Encryption Standard (AES) yields minimal delay, whereas fully homomorphic methods, although privacy-preserving, remain impractical for real-time permissionless blockchains and are not included in our design. We explicitly address regulatory compliance: personal health data are stored off-chain (e.g., Interplanetary File System [IPFS]), preserving the "right to erasure" via deletion of off-chain records, and we implement strict access controls to meet Health Insurance Portability and Accountability Act (HIPAA) security rules. The design includes validator selection rules that limit Sybil attacks by requiring costly work (or stake) and supports post-quantum cryptographic agility (e.g., Falcon signatures). We define our research question ("Can CUDA-accelerated PoW enable a high-performance yet compliant health data blockchain?") and hypothesize that GPU parallelism will yield substantial increases in speed. Results confirm our hypothesis: throughput and latency are significantly improved while preserving data privacy and compliance. This work makes a comprehensive contribution by detailing implementation methods, performance benchmarking, and analysis of security and legal requirements in a unified blockchain framework for healthcare.

Samia Sayed, Muammar Shahrear Famous, Rashed Mazumder, Risala Tasin Khan · 8 authors

Blockchain is an emerging technology that is being used to create innovative solutions in many areas, including healthcare. Nowadays healthcare systems face challenges, especially with security, trust, and remote data access. As patient records are digitized and medical systems become more interconnected, the risk of sensitive data being exposed to cyber threats has grown. In this evolving time for healthcare, it is important to find a balance between the advantages of new technology and the protection of patient information. The combination of blockchain–InterPlanetary File System technology and conventional electronic health record (EHR) management has the potential to transform the healthcare industry by enhancing data security, interoperability, and transparency. However, a major issue that still exists in traditional healthcare systems is the continuous problem of remote data unavailability. This research examines practical methods for safely accessing patient data from any location at any time, with a special focus on IPFS servers and blockchain technology in addition to group signature encryption. Essential processes like maintaining the confidentiality of medical records and safe data transmission could be made easier by these technologies. Our proposed framework enables secure, remote access to patient data while preserving accessibility, integrity, and confidentiality using Ethereum blockchain, IPFS, and group signature encryption, demonstrating hospital-scale scalability and efficiency. Experiments show predictable throughput reduction with file size (200 → 90 tps), controlled latency growth (90 → 200 ms), and moderate gas increase (85k → 98k), confirming scalability and efficiency under varying healthcare workloads. Unlike prior blockchain–IPFS–encryption frameworks, our system demonstrates hospital-scale feasibility through the practical integration of group signatures, hierarchical key management, and off-chain erasure compliance. This design enables scalable anonymous authentication, immediate blocking of compromised credentials, and efficient key rotation without costly re-encryption.

Kiran Deep Singh, Prabhdeep Singh, Ankita Gupta, Rohan Verma

The healthcare sector has vast untapped potential in data management in biotech, pharmaceutical companies, research centers, and other clinical institutions. Health research that involves access and analysis of individuals' health information can lead to a much-improved understanding, prevention, and treatment of health conditions. Blockchain's potential has been identified in various applications, including managing personal health data. There are extensive data sets that can advance patient care protocols and deepen the understanding of patient pathology, fostering the development of new treatments. However, there has always been a privacy concern, and the financial value of these datasets deters stakeholders from sharing their data. The regulatory body has provided protection in promoting patent rights and data sharing through initiatives like common health research data spaces and fair data principles. Trust in the healthcare industry is paramount, where the protection of patient information is critical. While patients can withdraw consent for data use in research, blockchain technology offers a solution for managing patient consent and facilitating the securing of the data. This research implements a smart contract system for patient consent management and data sharing amongst state holders, which includes patients, researchers, data controllers, and supercomputer owners. Unlike traditional healthcare data management models, this mechanism shifts power from data controllers to a consortium of stakeholders. This chapter proposes a permission blockchain and smart contract mechanism that can enhance data sharing and consent management in healthcare, offering a more flexible and secure approach to handling sensitive health data.

Abdullah Ayub Khan, Refka Ghodhbani, Abdulmajeed Alsufyani, Nawal Alsufyani · 5 authors

No abstract is available for this record.

Michael J. Diaz, Sai Batchu, Jasmine Tran, Arthur Samia · 5 authors

No abstract is available for this record.

Kelly Richdale

Abstract This chapter explores the transformative role of AI across various dimensions of healthcare, highlighting the interplay between analytical and generative AI in medical imaging, as well as the implications of generative AI in drug discovery and development. It reviews generative AI in the broader framework of P4 medicine—predictive, preventive, personalized, and participatory—and the potential of AI to improve health outcomes, while tackling the rising costs and increased demand for healthcare professionals in aging populations. The requirement for multimodal longitudinal datasets is examined, as well as the challenges around data sparsity and data bias, and the need for data equity. The chapter further evaluates inherent risks and the most relevant aspects of regulation relating to the use of AI in healthcare, personal data protection, and security. In conclusion, the chapter reviews the potential future impact of agentic AI, and technology convergence with robotics, zero-knowledge proofs and blockchain, and quantum computing.

Ravi Teja Potla

The increase in demand of data driven decision making in sensitive fields like healthcare and finance requires machine learning frameworks that maintain strict data privacy and follow regulations. Federated Learning (FL) provides a decentralized way to train models. It allows multiple organizations to learn together from distributed datasets without sharing raw data. But, traditional FL methods, such as Federated Averaging (FedAvg), face issues in real world situations. These issues arise from different data distributions among clients and the risk of information leaks from shared model updates. In this research study, we introduce a new federated learning framework with two main innovations: First the adaptive aggregation strategy that adjusts client contributions based on how stable they are and their quality, and second an optional differential privacy module at the server to make sure privacy guarantees. We tested the framework on two publicly available datasets: a heart disease dataset from the University of California, Irvine (UCI) repository and a large financial dataset from Kaggle. This simulates collaboration between hospitals and financial institutions. Experimental results show that our adaptive aggregation method boosts model accuracy by up to 4.2% compared to FedAvg, while still performing well even with differential privacy applied. The model achieves an AUC of 0.93 and an F1 score of 0.891, with minimal communication overhead. These results confirm the framework’s strength and its ability to support the ethical use of Artificial Intelligence in regulated and data sensitive areas. They also recommend it can scale effectively across larger federated networks.

Oyegoke Oyebode

The deployment of artificial intelligence in healthcare is increasingly constrained by privacy, equity, and regulatory compliance challenges, especially in multilingual and cross-border contexts.Traditional centralized machine learning approaches are limited by restrictions on patient data sharing, raising both ethical and legal concerns.Federated learning offers a promising solution by enabling distributed training across institutions without transferring raw data, yet ensuring trust and privacy in federated systems remains a critical barrier.This study proposes a novel framework that combines transformer architectures with encrypted federated datasets anchored by blockchain zero-knowledge proofs (ZKPs) to achieve privacy-preserving, equitable, and multilingual healthcare diagnostics.Transformer-based models, known for their strength in natural language processing and multimodal learning, are adapted to operate on encrypted federated datasets spanning diverse linguistic and demographic contexts.Blockchain provides a decentralized trust layer, while zero-knowledge proofs ensure verifiable model updates without exposing sensitive patient information.This combination allows healthcare providers to collaboratively train diagnostic models that maintain strong predictive performance while adhering to strict privacy guarantees.The framework also advances health equity by enabling multilingual diagnostics that address disparities in underrepresented populations.By integrating explainability mechanisms, stakeholders gain insights into model reasoning across diverse cultural and linguistic datasets.Case applications in federated medical imaging, multilingual clinical notes, and genomic diagnostics highlight the framework's capacity to balance accuracy, privacy, and fairness.Overall, the integration of transformers, federated learning, and blockchain ZKPs represents a pathway toward trustworthy and equitable AI-driven healthcare, enabling collaborative innovation while safeguarding patient rights.

Irum Matloob, Shoab Ahmed Khan, Bushra Bashir, Rukaiya Rukaiya · 6 authors

Healthcare recommendations and insurance have recently been one of the most emerging research areas in health informatics. The fraud in health insurance is becoming increasingly common day by day. To handle healthcare insurance fraud, there is an urgent need for an intelligent system that cannot only identify and monitor doctors' and hospitals' behavior regarding the health services they provide to patients but can also recommend doctors and hospitals to insured employees based on the quality of services they provided previously. This system creates patient and doctor profiles separately, based on their rating. The proposed system combines singular value decomposition (SVD), K-nearest neighbors based collaborative filtering (KNN-based CF), item-based collaborative filtering (Item-based CF), content-based filtering using term frequency-inverse document frequency (TF-IDF), and K-means clustering and probability distributions to recommend doctors and insurance plans. The system measures similarity scores between patients and doctors using cosine similarity, which helps to determine similarity scores and refine the recommendations. This study also uses blockchain technology to automate insurance claims reimbursement. The results are validated using real data from the employees of a local hospital. The system provides recommendations with a root mean square error (RMSE) value of 0.478 and a mean absolute error (MAE) value of 0.0422. The insurance plans developed using the proposed system have reduced the overall expenditure of the local hospital, with a reduction in total expenses. Blockchain technology further helps prevent healthcare fraud. In the proposed system, a healthcare insurance claims reimbursement system is built using smart contract technology on the Ethereum blockchain, ensuring security & transparency and lowering the number of healthcare frauds. The system includes roles for the insurance company, healthcare provider, and patients. It also provides a platform for claim submission, approval, or refusal. In Pakistan, no such system existed before recommending doctors from different hospitals based on their professional conduct or the good health services they provide.

Jhuma Dutta, Aakanksha Mishra, Sagarika Saha, Chayan Das · 6 authors

Diabetes is now an important global health problem. This paper presents a patient-oriented electronic health record system for predicting diabetes with machine learning. The diagnosis given by the doctor and the patient ID will be written into the Ethereum Smart Contract. Algorithms analyzed and compared using the Pima Indians Diabetes Dataset are Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and LightGBM. We have also used a decentralized, tamper-proof storage, InterPlanetary File System (IPFS), for storing the patient reports off-chain, and we will store the corresponding hash generated by the SHA-256 cryptographic hash algorithm in the smart contract, having gas optimization as well as scalability. Patients will use the Proof of Stake consensus mechanism-confirmed transparent smart contract transactions to grant or withdraw authorized user access permissions to their health data, with a One-Time Password (OTP) allowing for additional security. In our experimental result, the Support Vector Machine yielded the highest accuracy of 85.06%, f1-score of 0.7928, and a recall of 0.8148, outperforming all other algorithms. This work significantly contributes to privacy preservation by enhancing the existing approaches for diabetes diagnosis in healthcare while keeping the privacy of patient data.

Aleksandar Stankovic, Marina Marjanović

Artificial intelligence integration in healthcare platforms in synergy with software and hardware tools development offers great opportunities for daily improving healthcare. This research explores how much patient data is secured in healthcare applications and what impact their security can have on global healthcare. Accelerated integration of artificial intelligence in healthcare applications can be both useful and dangerous nowadays. Extremely sensitive data from AI-based applications are surely easy targets for attackers who can manipulate with AI/ML models. This paper will also present the potential dangers of modern healthcare applications in the 4.0 era and explores innovative methods for securing sensitive healthcare data, focusing on techniques such as blockchain, honeypots, zero-knowledge proofs (ZKP) and strategies to address adversarial attacks. We also present an extensive literature review and try to draw a parallel on possibilities in the implementation of security solutions in healthcare applications that use artificial intelligence. Our findings underscore the need for multidimensional security frameworks and provide concrete recommendations for the healthcare community. Ultimately, this paper bring our security solution and highlights the importance of adopting specific advanced security measures in line with the security challenges brought by using artificial intelligence.

Tanisha Bhardwaj, K Sumangali

With the rapid growth of healthcare data and the need for secure, interpretable, and decentralized machine learning systems, Federated Learning (FL) has emerged as a promising solution. However, FL models often face challenges regarding privacy preservation, transparency, and resistance to adversarial attacks. To address these limitations, this paper proposes the Privacy Preserving Federated Blockchain Explainable Artificial Intelligence Optimization (PPFBXAIO) framework, which integrates blockchain technology, Explainable AI (XAI), and optimization techniques to ensure privacy, traceability, and robustness in FL-based systems. PPFBXAIO employs Secure Hash Algorithm 256 (SHA-256) for blockchain-backed secure model updates, Min-Max normalization for feature scaling, and the Levy Grasshopper Optimization Algorithm (LGOA) for optimal feature selection and federated model tuning. The Entropy Deep Belief Network (EDBN) is used as the classifier to enhance classification accuracy and detect attacks. XAI tools like SHAP are utilized to improve model interpretability. Experimental validation was conducted using the Heart Disease dataset from Kaggle and the Wisconsin Breast Cancer dataset. Results showed that PPFBXAIO achieved 95.07% accuracy, 95.44% precision, 96.54% recall, 95.98% F1 score, and reduced training loss by 4.93% for Breast Cancer Wisconsin and achieved 93.07% accuracy, 91.19% precision, 95.39% recall, 93.24% F1 score for Heart Disease dataset. Proposed system has reduced latency by 81 ms, and improved throughput by 109 transactions per second for 100 rounds as compared to traditional models like FedAvg, FL-MPC, FL-RAEC, and PEFL. These results highlight the framework's superior performance, privacy preservation, and practical applicability in decentralized healthcare AI systems.

Dept of ECE IARE, Dadi Jagan Goud

Cryptocurrency price prediction has become crucial for informed trading decisions due to the volatile nature of assets like Bitcoin, Ethereum, Ripple, and Litecoin. Traditional methods like ARIMA and GARCH struggle with this volatility, while modern approaches such as machine learning and deep learning provide better accuracy. This study evaluates advanced models, including LSTM, GRU, and Light GBM, to predict cryptocurrency prices and assess trading strategies before and after the COVID-19 pandemic. GRU and LSTM excel at identifying patterns in price data, with GRU performing best for Ripple. Ensemble methods like Light GBM proved highly accurate for Bitcoin and Ethereum across time periods. Simpler models like RNN were sufficient for Ripple and Litecoin. The COVID-19 pandemic significantly impacted market dynamics, emphasizing the importance of precise predictions. Trading strategies based on model predictions showed that ensemble methods like Light GBM yielded the highest profitability post-pandemic. The findings highlight the need to tailor models to specific cryptocurrencies and market conditions. Improved deep learning tools can enhance trading efficiency and provide actionable insights for investors and policymakers. Future research could focus on predicting multiple cryptocurrencies simultaneously and optimizing portfolio-based trading strategies. Key Words: LSTM, ARIMA, GARCH, RNN

Shilpa Kottapally, Sr. Software Development Engineer, Adjudication - Rxclaim developement Application, CVS Health, 2100 E lake cook road , Buffalo grove Illinois 60047, USA

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Fausto Neri da Silva Vanin, Rodrigo da Rosa Righi, Cristiano André da Costa

Blockchain technology in healthcare is gaining attention for addressing data privacy, interoperability, and health record integrity issues. Standards like HL7 FHIR and OpenEHR ensure data consistency, but privacy concerns persist under regulations like HIPAA, GDPR, and LGPD. Existing methods often store only data hashes, raising validation risks. The MEPCA model introduces a blockchain-based framework for secure health record management, focusing on on-chain EHR data processing. Key elements include Data Steward, Shared Data Vault, and Zero-Knowledge Proofs of HL7 FHIR fields. Experiments with Fully Homomorphic Encryption show enhanced security and reliability for health records, offering a robust alternative to traditional off-chain approaches.

Alexandra Vultureanu‐Albişi, Costin Bădică, Mirjana Ivanović

The Internet of Things (IoT) paradigm is evolving and the Next-Generation IoT (NG-IoT) ecosystem will incorporate distributed ledger and blockchain technology, AI-adapted components, and intelligent edge solutions that take advantage of edge computing, Artificial Intelligence (AI), networks, and communications. In addition to the low integration of eXplainable Artificial Intelligence (XAI) in the IoT or NG-IoT contexts, the explainability of these systems is rarely evaluated. Due to these limitations, we thoroughly examined the current state of XAI integration with IoT services. We propose a new conceptual framework called eXING-IoT (eXplainability Integrated in the Next Generation IoT) for better NG-IoT systems' explainability integration and evaluation. This includes a list of qualities that future NG-IoT environments should have, thus paving the way for the advancement of NG-IoT beyond the state of the art.