Follow blockchain research across journals, conferences, and preprint repositories.
411 results · page 5 of 18
M Lakshmanan, G. S. Anandha Mala, K. M. Anandkumar
No abstract is available for this record.
Jamalur Rasheed, Shama Siddiqui, Imam, Mohammad
No abstract is available for this record.
Patrick Silva, Patrick J. Silva, Patrick A. Silva, Patrick A. Silva · 5 authors
Cloud and distributed computing, code repositories, and large language models are democratizing the less computationally intensive use cases of artificial intelligence (AI) in medicine. The convergence and democratization of these powerful tools promises to mobilize and utilize humanity’s knowledge and data, at least the knowledge bases and data that are readily available in the public commons. Healthcare represents a challenge due to fragmentation of the data fabric and governance mechanisms intrinsic to that sector of the economy. Privacy laws, stewardship practices, and the fragmented nature of the patient data journey (medical record silos) create cumbersome impediments to health data sharing, particularly longitudinal patient‐level data. Consequently, obtaining the data necessary to train and operationalize AI in many healthcare and clinical genomics use cases limits the promise of these new technologies in addressing complexities in healthcare. We posit that trust, provenance, and fitness of health data and transaction costs represent challenges that blockchain ledgers and smart digital contracts might address. Here, we present frameworks from some of the great economic thinkers that might help address some of the stewardship and agency issues inherent to health data sharing. Our goal is to promote a more equitable and patient‐centric healthcare data fabric to address current challenges of healthcare.
Cherukupally, Rushil Lingaiah
Background: Large Language Models (LLMs) like ChatGPT-4 Turbo, Claude 4 Sonnet, and DeepSeek-V3 are foundational to modern AI applications. However, a significant gap exists in understanding the direct link between their technical performance and user engagement, their scalability under concurrent load, and the practical performance cost of emerging privacy-preserving technologies. Objectives: This thesis conducts a holistic evaluation of these three leading LLMs to: (1) Compare their performance across latency, accuracy, and client-side resource utilization, and establish the relationship between these metrics and qualitative user engagement scores in various conversational contexts (RQ1). (2) Determine their scalability limits under concurrent user loads and quantify the performance overhead of integrating a zero-knowledge proof privacy protocol (EZKL) (RQ2). Methods: A custom, containerized Python framework was used to systematically test the models. For RQ1, performance and engagement were evaluated in three structured contexts: multi-turn (testing memory), cohesive (testing consistency), and ethical (testing safety) sessions. For RQ2, scalability was measured using Locust to simulate 25 to 200 concurrent users in both a standard centralized setup and a privacy-enhanced EZKL configuration. Key metrics included throughput (RPS), error rates, latency (median and P99), client-side resource consumption, and ZKP generation/verification times. Results: For RQ1, ChatGPT-4 Turbo emerged as the top generalist, showing the best balance of low latency, high accuracy, and strong engagement scores in dynamic multi-turn sessions (e.g., 7.9 personalization score). Claude 4 Sonnet excelled in specialized tasks, achieving a perfect context-switching score (0.0) in cohesive sessions and the highest Harm Avoidance Score (8.0) in ethical sessions, albeit with higher resource usage. DeepSeek-V3 consistently showed the highest latency and resource consumption, negatively impacting its engagement scores. For RQ2, ChatGPT-4 Turbo was the most scalable, peaking at 210 RPS with the lowest error rate. The integration of the EZKL protocol resulted in a catastrophic performance collapse for all models, with throughput dropping to near-zero and latency increasing to hundreds of thousands of milliseconds, rendering it unviable for real-time applications. Conclusions: The study concludes that model selection is highly use-case dependent: ChatGPT-4 Turbo is optimal for scalable, general-purpose applications; Claude 4 Sonnet is superior for high-stakes tasks requiring safety and precision. The findings empirically demonstrate that superior technical performance is a direct enabler of higher user engagement. Finally, current zero-knowledge proof implementations impose a prohibitive performance cost for interactive, scalable AI systems.
Biagio Boi, Christian Esposito
No abstract is available for this record.
Nasim Nezhadsistani, Naghmeh Sadat Moayedian, Burkhard Stiller
Advances in Internet of Medical Things technology, information and communication technologies, and machine learning have initiated the shift in healthcare towards smart healthcare. Centralization of health data to train ML models does pose privacy, ownership, and regulatory problems. Federated learning solves such problems by distributing the learning process to several devices, but it also encounters problems like encouraging participants and model aggregation correctness. Combining blockchain and FL can solve such problems through a decentralized approach that provides greater security and privacy for intelligent healthcare. This survey provides a systematic review of blockchain-based federated learning (BCFL) systems in healthcare. Key design features of BCFLs are analyzed, such as consensus protocols, crypto protocols, storage topology, and integration processes relevant to healthcare use cases. Characteristics such as convergence delay, computation overhead, accuracy loss when privacy is an issue, and ledger scalability for different implementations are compared among common implementations. The works of recent FL-based healthcare frameworks have been discussed along with determining the challenges and research directions for healthcare use cases.
Petar Radanliev
Frontier AI systems, including large-scale machine learning models and autonomous decision-making technologies, are deployed across critical sectors such as finance, healthcare, and national security. These present new cyber-risks, including adversarial exploitation, data integrity threats, and legal ambiguities in accountability. The absence of a unified regulatory framework has led to inconsistencies in oversight, creating vulnerabilities that can be exploited at scale. By integrating perspectives from cybersecurity, legal studies, and computational risk assessment, this research evaluates regulatory strategies for addressing AI-specific threats, such as model inversion attacks, data poisoning, and adversarial manipulations that undermine system reliability. The methodology involves a comparative analysis of domestic and international AI policies, assessing their effectiveness in managing emerging threats. Additionally, the study explores the role of cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, in enhancing compliance, protecting sensitive data, and ensuring algorithmic accountability. Findings indicate that current regulatory efforts are fragmented and reactive, lacking the necessary provisions to address the evolving risks associated with frontier AI. The study advocates for a structured regulatory framework that integrates security-first governance models, proactive compliance mechanisms, and coordinated global oversight to mitigate AI-driven threats. The investigation considers that we do not live in a world where most countries seem to be wishing to follow European Union ideals, and in the wake of this particular trend, this research presents a regulatory blueprint that balances technological advancement with decentralised security enforcement.
Rahul kumar -, Chin‐Shiuh Shieh, Prąsun Chakrabarti, Ashok Kumar · 6 authors
Federated Learning (FL) transformed decentralized machine learning by allowing joint model training without mutually sharing raw data, hence being especially useful in privacy-sensitive applications like healthcare, e-commerce, and finance. Even with its privacy-focused architecture, FL is vulnerable to a range of security attacks such as data poisoning, model inversion, membership inference attacks, and communication interception. These attacks compromise the confidentiality of patients in healthcare, consumer data privacy in e-commerce, and financial safety in banking, thus necessitating effective privacy-preserving mechanisms. This survey presents a classification of security threats in FL, grouping them by their source, effect, and attack mode. We review state-of-the-art countermeasures, such as differential privacy, secure multi-party computation, homomorphic encryption, and resilient aggregation methods, their effectiveness, trade-offs, and real-world applicability to FL. In medicine, FL enables joint disease diagnosis without compromising patient confidentiality; in online shopping, it provides personalized suggestions without revealing customer tastes; and in banking, it improves fraud detection without violating regulatory requirements. In addition, we discuss future horizons in privacy-preserving FL, including adversarial robustness, blockchain-protected models, and tailored FL architectures, improving security and resiliency in these domains. We also discuss the balancing problems between security, accuracy, and computational efficiency with possible trade-offs in scaling privacy-preserving FL By analyzing threats and mitigation strategies systematically, this paper will provide direction to future research on designing secure, scalable, and privacy-preserving FL frameworks for the changing healthcare, e-commerce, and finance needs.
Pranav Bawgikar, K. J. Devaiah, G. Yogdeep, V. Revathi
No abstract is available for this record.
Mr. Amar More, Mr . Karan More, Mr . Nikhil Neavse, Mr. Prasanna Deokar · 6 authors
This project introduces a decentralized file storage system that leverages blockchain technology to create a secure, immutable, and tamper-resistant platform for file sharing. By storing files within blocks on a blockchain, the system ensures that once data is uploaded, it cannot be altered or deleted, making it ideal for applications where data integrity is critical. Users interact with the platform through a web interface, allowing them to upload, download, and share files across a peer-to-peer network. The blockchain structure used in this project employs a Proof of Work (PoW) consensus mechanism, requiring peers (miners) to solve cryptographic puzzles to validate blocks and add them to the chain. Two different PoW methods are used: one generates nonces at random, while the other increases the nonce value one after the other. By comparing the effectiveness and security of different methods, the project finds that random nonce generation outperforms them at higher difficulty levels, providing quicker block validation and more robust defense against possible assaults. On the other hand, the incremental approach is less secure over time because it is simpler to foresee. The project also covers the advantages of on-chain storage, which involves storing files directly inside blockchain blocks. This approach offers better security but comes at the expense of more processing power. Furthermore, it investigates alternatives such as off-chain blockchain architectures for more effective file storage in subsequent iterations and Proof of Stake (PoS) for lowering resource use
Anna Visvizi, Igor Calzada, Krzysztof Kozłowski, Orlando Troisi
No abstract is available for this record.
Wulf A. Kaal
Contemporary Artificial Intelligence ("AI") systems, particularly Large Language Models ("LLMs"), face an imminent shortage of high-quality, humangenerated textual data, a phenomenon often termed "data exhaustion".This article examines the limitations of existing centralized data-annotation frameworks, highlighting critical issues such as bias, high computational overhead, and insufficiently adaptive infrastructures.Current market participants-including Scale AI, Appen, CloudFactory, and others-excel at rapidly scaling annotation services yet struggle with ethical sourcing, privacy compliance, and equitable compensation.In addition, legal and regulatory concerns, exemplified by stringent mandates such as the General Data Protection Regulation ("GDPR"), constrain the free flow of data essential for advanced AI research.As a corrective measure, decentralized data production paradigms are proposed, including the adoption of smart contracts, token-based incentives, and participatory governance through Decentralized Autonomous Organizations ("DAOs").While existing decentralized initiatives-SingularityNET, Fetch.ai,Ocean Protocol, Numeraire, and DcentAI-offer incremental innovations in reputation management and stakeholder engagement, they fail to fully address the nuanced requirements of large-scale "Mechanical Turk"-style data creation.In contrast, the author proposes a Weighted Directed Acyclic Graph ("WDAG") governance model which provides a multi-dimensional reputation framework, facilitating real-time validation of data contributions, adaptive ethical and legal compliance, and collaborative oversight by diverse community members.Findings suggest that such WDAGcentric systems can more effectively maintain data quality, ensure ethical alignment, and incentivize broad participation, thereby mitigating the looming data shortage and expanding AI's societal benefits.Ultimately, successful implementation requires coordinated efforts among policymakers, industry practitioners, and civil society actors to sustain both the technological and ethical integrity of AI research.By integrating WDAG-based governance with emerging decentralized solutions, the AI community may realize a more equitable, scalable, and future-ready paradigm for data provisioning.
Maria Cristina Enache
Emerging digital technologies such as Artificial Intelligence (AI), blockchain, Non-Fungible Tokens (NFTs), cryptocurrencies, and the metaverse have radically altered the landscape of industries worldwide. As these technologies continue to evolve, understanding how younger generations perceive and interact with them can offer valuable insights into future adoption trends. In this article, we present a detailed theoretical explanation of these technologies, paired with a comprehensive statistical analysis based on survey data from Romanian students. By applying advanced statistical methods such as correlation analysis, comparative analysis, and cluster segmentation, we aim to uncover not just familiarity and interest but also the underlying factors that shape students’ attitudes toward these groundbreaking technologies.
Tsung‐Chih Hsiao, Tzer‐Long Chen, Shu-Chen Chang, Tsui-Ping Chang
This research explores how integrating Omnipresent AI and smart contracts enhances the efficiency of medical insurance claims in commercial health insurance. AI enables real-time data processing, instantly accessing and evaluating medical records from secure ledgers. Blockchain technology integrates these records with smart contract policies for swift, accurate assessments. Smart contracts, hosted on a third-party certification platform, authenticate, grant access, and document the process, eliminating manual application for records. This streamlines claim reviews, enabling immediate payouts to policyholders. By leveraging AI, blockchain, and smart contracts, this approach optimizes efficiency, reduces costs, and accelerates settlements for insured beneficiaries.
Khyati Kapadiya, Fenil Ramoliya, Keyaba Gohil, Usha Patel · 9 authors
No abstract is available for this record.
Murali Krishna Pasupuleti
Abstract: This chapter delves into the transformative synergies between quantum computing, artificial intelligence (AI), and blockchain technology, focusing on their revolutionary impact on security, decentralized systems, and biomedical science. Quantum computing’s unparalleled computational power, combined with AI’s predictive analytics, enhances data security through quantum-resistant cryptography and advanced threat detection systems. In blockchain, the integration of quantum and AI optimizes scalability, improves transaction efficiency, and fortifies decentralized networks against quantum attacks. In biomedical science, these technologies accelerate drug discovery, enable precise genomic analysis, and enhance personalized medicine through AI-driven insights and quantum simulations. The chapter also addresses key challenges such as data privacy, ethical considerations, and scalability issues, while showcasing real-world applications and success stories in industries like healthcare, finance, and IoT. It concludes with a forward-looking perspective on fostering interdisciplinary collaboration and innovation to harness these quantum synergies for global societal benefit. Keywords Quantum computing, artificial intelligence, blockchain, quantum cryptography, decentralized systems, data security, AI-driven analytics, biomedical science, drug discovery, personalized medicine, quantum-resistant blockchain, genomic analysis, quantum simulations, interdisciplinary collaboration, innovation.
Aarya Nimsadkar, Anil M. Pethe
Blockchain has tremendous power supply chain (SC) operations in order to change from, originating SC, re-engineering business operations to meliorate security. The ultimate objective of this article is to furnish crucial understanding of the association of blockchain in pharmaceutical supply chains (PSC) and furthermore develop a conceptual structure for application in the pharmaceutical industry. Blockchain technology is being widely use in supply chain management since it prevents the forfeiting of the drugs in the distribution chain. Mostly concentrating upon employing blockchain to deal with industry-specific problems such as patient privacy, regulation, and clinical research, as well as drug counterfeiting and recall challenges. We emphasize the corresponding advantages, potential social effects, and the most advanced technology available today as well as key trends and challenges. Blockchain-based technology can successfully improve healthcare by enhancing the visibility and management of operations.
Hoda Hamouda, Victoria L. Lemieux
The decentralized applications and distributed ledgers of the blockchain technology (BT) make the exchange of health records more secure, allow users to be the primary owners of their health records, and provide higher protection of users’ records due to an ability to exchange information without revealing their identifiable information. The paper discusses what human-centered design (HCD) methods revealed about the user experience of individuals interacting with a BT-based solution that lets users contribute their de-identified health data to research projects in precision medicine. The methods revealed challenges in the user experience and presented the solutions carried out throughout the iteration phases of the solution’s user experience. Despite the privacy-preserving benefits of blockchain-based platforms, the complicated architecture of the technology and management of BT wallets constitute a real challenge to designing a user-friendly experience. This negatively impacts the adoption and implementation of BT-based solutions in health records management.
Bilal A. Salih Ozturk, Huda Kadhim Tayyeh, Heba Emad Namiq, Hemant B. Mahajan · 10 authors
ABSTRACT Cloud‐based Electronic Health Records (EHRs) have seen a substantial increase in usage in recent years, especially for remote patient monitoring. Researchers are interested in investigating the use of Healthcare 4.0 in smart cities. This involves using Internet of Things (IoT) devices and cloud computing to remotely access medical processes. Healthcare 4.0 focuses on the systematic gathering, merging, transmission, sharing, and retention of medical information at regular intervals. Protecting the confidential and private information of patients presents several challenges in terms of thwarting illegal intrusion by hackers. Therefore, it is essential to prioritize the protection of patient medical data that is stored, accessed, and shared on the cloud to avoid unauthorized access or compromise by the authorized components of E‐healthcare systems. A multitude of cryptographic methodologies have been devised to offer safe storage, exchange, and access to medical data in cloud service provider (CSP) environments. Traditional methods have not been effective in providing a harmonious integration of the essential components for EHR security solutions, such as efficient computing, verification on the service side, verification on the user side, independence from a trusted third party, and strong security. Recently, there has been a lot of interest in security solutions that are based on blockchain technology. These solutions are highly effective in safeguarding data storage and exchange while using little computational resources. The researchers focused their efforts exclusively on blockchain technology, namely on Bitcoin. The present emphasis has been on the secure management of healthcare records through the utilization of blockchain technology. This study offers a thorough examination of modern blockchain‐based methods for protecting medical data, regardless of whether cloud computing is utilized or not. This study utilizes and evaluates several strategies that make use of blockchain. The study presents a comprehensive analysis of research gaps, issues, and a future roadmap that contributes to the progress of new Healthcare 4.0 technologies, as demonstrated by research investigations.
Caspar Barnes, Mateo Aboy, Timo Minssen, Jemima Winifred Allen · 7 authors
Participation in research is supposed to be voluntary and informed. Yet it is difficult to ensure people are adequately informed about the potential uses of their biological materials when they donate samples for future research. We propose a novel consent framework which we call "demonstrated consent" that leverages blockchain technology and generative AI to address this problem. In a demonstrated consent model, each donated sample is associated with a unique non-fungible token (NFT) on a blockchain, which records in its metadata information about the planned and past uses of the sample in research, and is updated with each use of the sample. This information is accessible to a large language model (LLM) customized to present this information in an understandable and interactive manner. Thus, our model uses blockchain and generative AI technologies to track, make available, and explain information regarding planned and past uses of donated samples.
Raja Krishnamoorthy, K. P. Kaliyamurthie
The safe and smooth transfer of data across intercepting nodes is a crucial component of data processing in the medical field. Transmitting error-free, unduplicated data is achievable when third-party entities are effectively eliminated and direct connections between the patient and healthcare provider are maintained. Blockchain technology offers a secure method for exchanging information through nodes and connections, ensuring the safety of transactions and potentially addressing current limitations. Currently, the medical data exchange is provider-centric, insecure, sluggish, and often incomplete. These issues arise from fundamental, structural, and semantic inoperability, which impede data interchange. By utilizing blockchain technology with the appropriate markers, patient data security during transfer can be ensured. This research assesses the possibility of future use of distributed ledger technology in mobile healthcare settings.
P. Ramya, A Pooja, K. Anitha Kumari
Worldwide diabetes prevalence is rising, which emphasizes the necessity for safe health monitoring methods. The present study investigates the potential of implementing Ring Learning With Errors (RLWE) encryption in a smart glucometer to augment security and privacy in the context of diabetes care. Sensitive health data is encrypted and stored securely with RLWE, a lattice-based cryptography method. The Ethereum blockchain is used by the smart glucometer to provide decentralized and unchangeable data management, and RLWE encryption is included for data security. Ethereum smart contracts offer strong security features by ensuring data integrity and access management. Performance criteria, such as memory utilization and encryption or decryption times, evaluate the usefulness of RLWE in real-world medical equipment. The study highlights RLWE’s effectiveness in preventing unauthorized access to sensitive data and shows notable developments in secure health information systems. The potential of blockchain technology and RLWE efficiency over LWE to improve healthcare data security and the same is highlighted by the results obtained through the various calculations done. The study advances patient care and data security in healthcare settings by promoting safe and reliable medical IoT devices through the use of advanced cryptographic algorithms and decentralized data management.
Yazan Otoum, C. Hu, Eyad Haj Said, Amiya Nayak
Federated learning offers a framework for developing local models across institutions while safeguarding sensitive data. This paper introduces a novel approach for heart disease prediction using the TabNet model, which combines the strengths of tree-based models and deep neural networks. Our study utilizes the Comprehensive Heart Disease and UCI Heart Disease datasets, leveraging TabNet’s architecture to enhance data handling in federated environments. Horizontal federated learning was implemented using the federated averaging algorithm to securely aggregate model updates across participants. Blockchain technology was integrated to enhance transparency and accountability, with smart contracts automating governance. The experimental results demonstrate that TabNet achieved the highest balanced metrics score of 1.594 after 50 epochs, with an accuracy of 0.822 and an epsilon value of 6.855, effectively balancing privacy and performance. The model also demonstrated strong accuracy with only 10 iterations on aggregated data, highlighting the benefits of multi-source data integration. This work presents a scalable, privacy-preserving solution for heart disease prediction, combining TabNet and blockchain to address key healthcare challenges while ensuring data integrity.
Nitin Rathore, Aparna Kumari, Margi Patel, Alok Sinh Chudasama · 7 authors
ABSTRACT In this article, we delve into the intersection of blockchain technology and artificial intelligence (AI) to fortify the security of electronic health records (EHRs). Existing EHR systems face challenges like interoperability issues, user interface complexities, and data security concerns, hindering seamless patient care and healthcare delivery. This article thoroughly explores the hurdles associated with EHR security and investigates the potential of employing blockchain and AI solutions to mitigate these challenges. The review underscores the necessity for resilient, interoperable systems to protect sensitive health information by pinpointing opportunities for collaborative strategies. In essence, this article contributes valuable perspectives on the dynamic landscape of EHR security, guiding future research and development at the crossroads of blockchain; we presented a case study on EHR security, customized care, predictive analytics, and more efficient healthcare delivery. Using case studies as illustrative examples, the article scrutinizes practical applications, shedding light on successful implementations and areas requiring refinement. Several outcomes for the predictive analytics of patients' surgeries are shown in this case study. Next, we compare blockchain‐based EHR systems with existing, non‐blockchain‐based EHR systems. Finally, we present the concluding remarks with future directions for integrating blockchain, AI, and EHR systems.