Blockchain Papers

Xiao-Yang Liu, Rongyi Zhu, Daochen Zha, Jiechao Gao · 7 authors

The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science. However, concerns regarding data privacy have emerged, especially when multiple stakeholders aim to collaboratively enhance LLMs using sensitive data. In this scenario, federated learning becomes a natural choice, allowing decentralized fine-tuning without exposing raw data to central servers. Motivated by this, we investigate how data privacy can be ensured in LLM fine-tuning through practical federated learning approaches, enabling secure contributions from multiple parties to enhance LLMs. Yet, challenges arise: (1) despite avoiding raw data exposure, there is a risk of inferring sensitive information from model outputs, and (2) federated learning for LLMs incurs notable communication overhead. To address these challenges, this article introduces DP-LoRA, a novel federated learning algorithm tailored for LLMs. DP-LoRA preserves data privacy by employing a Gaussian mechanism that adds noise in weight updates, maintaining individual data privacy while facilitating collaborative model training. Moreover, DP-LoRA optimizes communication efficiency via low-rank adaptation, minimizing the transmission of updated weights during distributed training. The experimental results across medical, financial, and general datasets using various LLMs demonstrate that DP-LoRA effectively ensures strict privacy constraints while minimizing communication overhead.

Harsh Bansal, Divya Gupta, Darpan Anand

Healthcare industry is constantly evolving because of rapid technological breakthroughs and changing patient needs. Use of innovations in healthcare using modern technologies is revolutionizing the delivery of health care, enhancing patient outcomes, and boosting operational effectiveness, data security, and privacy. Telemedicine and remote patient monitoring have become prominent, especially after the COVID-19 pandemic. Issues with traditional telemedicine and remote patient monitoring, which rely majorly on technology such as privacy and security concerns, interoperability issues, decision making based on large amounts of health data generated can be resolved using technologies such as blockchain and artificial intelligence wherein blockchain ensures decentralization, immutability of data facilitating easy data sharing, and consent administration and artificial intelligence enables clinical decision support decision and personalized healthcare with a focus on preventive instead of reactive health care ( Gupta, S., et al., 2023 ). This chapter reviews the use of blockchain and AI in some significant telemedicine and remote patient monitoring projects and how they can improve the efficiency and functionality of current healthcare. A patient-centric framework has been proposed using components of blockchain and artificial intelligence such as decentralization, distributed ledger technology, consensus mechanisms, smart contracts, immutability, machine learning, deep learning in telemedicine, and remote patient monitoring. Major challenges such as legal and regulatory considerations, ethical concerns, integration and implementation, adoption, and acceptance have been identified and addressed. Overall, the use of blockchain and artificial intelligence in telemedicine and remote patient monitoring has a huge potential to transform the way healthcare is provided.

Poornima G. Naik, Vidya Laxmikant Badadare, Rajani S. Kamath

In today's digital age, the healthcare sector is experiencing a significant transformation due to the pervasive influence of Information Technology (IT). There is an abundance of sources for generating and accessing patients' health data, and safeguarding the management of this data has become of utmost importance. Blockchain technology, at the forefront of technological innovation, offers a promising solution for secure data storage within the healthcare domain. In the context of current research the authors, have developed a sophisticated smart contract designed specifically for the storage and management of patients' medical records. This smart contract incorporates a range of functionalities, including the creation of new records, authorization for newly generated records, and the ability to retrieve stored records. Uploading the test reports on IPFS server has been deferred for consideration in future scope. The smart contract is meticulously designed and rigorously tested on the Ethereum Blockchain framework. The research objective is to elucidate the pivotal role that blockchain technology plays in ensuring the security and integrity of healthcare recordkeeping, utilizing the robust Ethereum framework as authors’ platform of choice. The research aims to shed light on the transformative potential of blockchain in enhancing the trustworthiness and privacy of patient data within the healthcare sector.

Elizabeth A Tissier, A. Berglund, Gabrielle J Johnson, Zakary A Sanzone · 8 authors

Background and objective Physician credentialing and verification in the medical education setting are challenging for the modern workforce. The credentials verification process may be time-consuming and challenging for participants. Blockchain technology is a potential resource for authenticating records with reduced administrative burden and time spent. This study investigates whether the use of blockchain technology reduces the time until verification of a participant's credentials. Methods An anonymous letter designation was assigned to 23 medical students. All students enrolled in, and completed, a course designed and run by the Edward Via College of Osteopathic Medicine at Auburn (VCOM) as part of the routine medical education curriculum. At the completion of the training, a credentials certificate was produced, which showed course completion. The anonymous letter designation was utilized in the creation of the certificates. The letter designations were shared with an anonymous investigator. No student names were shared with the investigator. The investigator posed as an employing/credentialing entity and contacted VCOM to record the time required to verify the credentials certificate indicating course completion. The elapsed time until credentials verification was completed for each student in the current system (CS) was recorded. Subsequently, the credentials certificate was minted as a blockchain-based, non-fungible token (NFT) and uploaded to a document software management system. An investigator again posed as an employing/credentialing entity and utilized this system to verify the credentials of the 23 students in the study using the NFT system. The times elapsed until verification of credentials were recorded as the NFT pathway. Data from the NFT pathway and non-NFT pathway were compiled and reviewed. Results Data were normally distributed per the Andersen-Darling Test. A t-test (Welch's method) was performed. The mean time of 111,214 seconds (30.89 hours or 1.29 days) in the CS varied significantly from the mean time of 14 seconds in the NFT blockchain system (p<0.01). The standard deviation of 56,568 seconds in CS varied significantly from 9.9178 seconds in the NFT blockchain (p<0.01). Conclusions The NFT/blockchain system reduces the mean time until the credential verification is completed and reduces the variance seen in time until credentialing is completed. The NFT/blockchain system may significantly bring down the administrative burden and time spent in the credentialing process.

G. Senthilkumar, Aravindan Srinivasan, J. Venkatesh, Ramu Kuchipudi · 6 authors

Even if big cities are working globally to build the infrastructure for smart cities and there is a greater emphasis on the security of electronic health records, patient privacy is frequently compromised. Previous attempts to combat this have left patients with largely unavailable data. Currently used record-management systems struggle to strike a balance between data privacy and patient and provider access. Blockchain, a new technology, the ability to share data in a decentralized and transactional manner. To balance the accessibility and privacy of electronic health records, blockchain technology may be applied in the healthcare industry. The blockchain-based architecture we present in this work enables patients, healthcare professionals, and third parties to access medical information in a secure, efficient, and straightforward manner while maintaining the privacy of sensitive patient data. Modern cryptographic techniques are used in our architecture, Ancile, to increase security. The usage of smart contracts developed on the Ethereum blockchain improves access control and data obscuration. This article will look at how Ancile works with the varied needs of patients, providers, and third parties in order to understand how the framework might address reoccurring privacy and security concerns in the healthcare industry.

Sophiya Rumovskaya, Andrey Litvin

Blockchain and artificial intelligence (AI) are two of the most disruptive modern technologies. A blockchain is a distributed ledger capable of storing data in blocks. The data stored on a blockchain is immutable and secured with cryptography. The blockchain can guarantee data security without the involvement of a third party. The integration of it with AI to create distributed artificial intelligence (DAI) is increasingly being used in various areas of human activity. AI can increase the efficiency of blockchains by streamlining computations and processes, reducing the burden on miners and decreasing latency. The latter results in faster transactions and reduced carbon footprint of the blockchain technology. Blockchain can help develop explainable AI by means of accessing an immutable record of unstructured medical data and processes used by the system in its decision-making process. The purpose of this narrative review is to analyze the possibilities of using a decentralized AI in medicine.

Ghazi Farouk, Tareck Alsamara

This article presents an in-depth study of the legal landscape surrounding blockchain technology in the healthcare sector, with a special focus on case studies from European countries. Analyzing the existing legal framework and regulations, the research highlights the challenges and opportunities associated with the adoption of blockchain in healthcare. The most important research areas are data protection, security, consent, liability, and compliance. Through a comparative analysis of various European countries, the article illuminates the differences in legal approaches and points out possible areas of harmonization. The results clarify the legal aspects that must be addressed to ensure the integration of blockchain technology into healthcare systems, innovation while protecting patients' rights, and compliance with regulatory requirements.

Naresh Kshetri, James Hutson, G. Revathy

Blockchain as a digital ledger for keeping records of digital transactions and other information, it is secure and decentralized technology. The globally growing number of digital population every day possesses a significant threat to online data including the medical and patients’ data. After bitcoin, blockchain technology has emerged into a general-purpose technology with applications in medical industries and healthcare. Blockchain can promote highly configurable openness while retaining the highest security standards for critical data of medical patients. Referred to as distributed record keeping for healthcare systems which makes digital assets unalterable and transparent via a cryptographic hash and decentralized network. The study delves into the security and safety improvement associated with implementing blockchain in AI-based healthcare systems. Blockchain-enabled AI tackles the existing issues related to security, performance efficiencies, and safety in healthcare systems. This study also examined the implementation of Artificial Intelligence (AI) in healthcare and medical industry, potential areas, open questions concerning the blockchain in healthcare systems. Finally, the article proposed an AI-based healthcare blockchain model (healthAIChain) to improve patients’ data and security.

Nima S. Ghorashi, Murwarit Rahimi, Reza Sirous, Ramin Javan

INTRODUCTION: Although blockchain technology and smart contracts are garnering attention in various sectors, their applications and familiarity within the realm of radiology remain largely unexplored. Blockchain, a decentralized digital ledger technology, offers secure, transparent, and resilient data management by distributing the verification process across a network of independent entities. This decentralized technology presents a possible solution for a range of healthcare challenges, from secure data transfer to automated verification processes. To address such challenges in the context of medical imaging, blockchain could provide different approaches, including smart contracts, machine learning algorithms, and the secure dissemination of large files among key stakeholders such as patients, healthcare providers, and institutions. This manuscript aims to explore the current attitudes and perspectives of trainees and radiologists to the utilization of blockchain technology and smart contracts in clinical radiology. Additionally, the study provides an in-depth analysis of the potential applications for incorporating blockchain into radiology. METHODS: After obtaining The George Washington University Committee on Human Research Institutional Review Board (IRB) approval, we conducted a 10-question survey among radiologists and trainees at several institutions and private practices. Surveys were created via the Google Forms application and were emailed to potential participants. Participants were asked about their current academic level (medical student, resident/fellow, academic radiologist, private practice radiologist, others), their knowledge level about the field of imaging informatics and blockchain and smart contract technologies, their level of interest in learning more about blockchain and smart contracts, and their opinion about possible applications of blockchain and smart contract in the future of medical imaging. RESULTS: A total of 118 survey requests were distributed; 83 were returned, reflecting a 70.3% overall response rate. Of these, 19 were sent to private practices with a 15.8% response rate (3/19), and 99 to academic centers, yielding an 80.8% response rate (80/99). The survey respondents demonstrated a strong interest and need to further understand these technologies among radiologists and trainees. This study focuses on key components of this technology as it relates to healthcare and the practice of radiology, including data storage, patient care, secure communication, and automation, as well as strengths, weaknesses, opportunities, and threats (SWOT) analysis. DISCUSSION: To our knowledge, this is the first study to investigate and establish a baseline for the current perspectives on the application of blockchain technology and smart contracts in clinical radiology amongst trainees and radiologists across academic and private settings. Incorporating blockchain and smart contracts technologies into the field of radiology has the potential to achieve greater efficiency, security, and patient empowerment. However, the adoption of this technology comes with challenges, such as infrastructure, interoperability, scalability, and regulatory compliance. Collaboration between radiologists, hospital administration, policymakers, technology developers, and patient advocacy organizations will help guide and advance our understanding of the potential applications of blockchain and smart contracts in radiology and healthcare.

Ritik Kashwani, Hemant Sawhney

Blockchain technology and the metaverse have the potential to revolutionize dentistry and healthcare by enhancing data security, patient empowerment, disaster victim identification, and the delivery of dental services. In this review, we discuss the current state of the art in the field of dentistry and the future of dentistry, highlighting the advantages and challenges of utilizing blockchain technology for disaster victims’ identification. Blockchain's applications in Disaster Victim Identification (DVI) offer a humanitarian dimension, helping bring solace to families in times of tragedy. Moreover, blockchain's potential to establish virtual health clinics and telemedicine platforms could bridge healthcare gaps in underserved regions.

Abdullah Lakhan, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Mohd Khanapi Abd Ghani · 8 authors

No abstract is available for this record.

Deepansha Chhabra, Meng Kang, Victoria L. Lemieux

This chapter contributes to research on the issue of data double spending, or unauthorized secondary use of individuals&s; data. The chapter describes two solutions that provide decentralized marketplaces for individuals to share their health data for purposes of AI-driven health research – one that uses the Hyperledger Indy/Aries protocol – the ‘Self-Sovereign’ data marketplace – and the other an Ethereum-based solution – the ‘Ocean Protocol’ data marketplace. Based on an implementation of applications using both protocols, the chapter evaluates the strengths and weaknesses of each data marketplace vis-a-vis adherence to fair data processing principles and protecting individuals from data double spending. The chapter contributes to a clearer articulation of fair blockchain-based data processing and the issue of data double spending, an assessment of how well each solution addresses the issue, and possible directions for research aimed at preventing data double spending.

Christopher P. Albertyn, Svitlana Surodina, Bo Tan, Tina Woods · 7 authors

UNSTRUCTURED Global healthcare systems need to evolve to ensure optimal, safe, and ethical utilisation of health data and the latest digital technologies, such as Artificial Intelligence (AI), Privacy Enhancing Technologies (PETs), and Distributed Ledger Technologies (DLTs), to meet the challenges of a global ageing population. Simultaneously, the increasing capabilities of remote measurement technologies and the proliferation of 5G networks demonstrates that digital technologies are now more accessible to a much larger population, offering an opportunity for decentralised democratised health data use that supports individual agency. Given the significant international human and economic cost of cognitive decline and dementia, we propose that a person-centred decentralised health data ecosystem, underpinned by these emerging technologies and opportunities, would reduce burden on cognitive healthcare systems by intervening earlier, accelerate clinical research innovation in dementia, and extend cognitive healthspan. Crucially, we argue for the importance of including the individual, as well as other key stakeholders, in the development, continuing operation, and as a shared beneficiary of any potential accrued value emerging from this ecosystem.

Xueping Liang, Juan Zhao, Yan Chen, Eranga Bandara · 5 authors

BACKGROUND: Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site. OBJECTIVE: This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs. METHODS: We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain. RESULTS: We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra-based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis. CONCLUSIONS: Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain.

Hakima Rym Rahal, Sihem Slatnia, Okba Kazar, Ezedin Barka · 5 authors

No abstract is available for this record.

Rabimba Karanjai, Edward Li, Lei Xu, Weidong Shi

The introduction of large language models (LLMs) like ChatGPT and Google Palm2 for smart contract generation seems to be the first well-established instance of an AI pair programmer. LLMs have access to a large number of open-source smart contracts, enabling them to utilize more extensive code in Solidity than other code generation tools. Although the initial and informal assessments of LLMs for smart contract generation are promising, a systematic evaluation is needed to explore the limits and benefits of these models. The main objective of this study is to assess the quality of generated code provided by LLMs for smart contracts. We also aim to evaluate the impact of the quality and variety of input parameters fed to LLMs. To achieve this aim, we created an experimental setup for evaluating the generated code in terms of validity, correctness, and efficiency. Our study finds crucial evidence of security bugs getting introduced in the generated smart contracts as well as the overall quality and correctness of the code getting impacted. However, we also identified the areas where it can be improved. The paper also proposes several potential research directions to improve the process, quality and safety of generated smart contract codes.

Yi Yang, Jiaxuan Wei, Zhixuan Yu, Ruisheng Zhang

No abstract is available for this record.

Badal Gami, Manav Agrawal, Deepak Mishra, Danish Quasim · 5 authors

Abstract While blockchain technology (BT) is considered secure, there are several vulnerabilities that can breach its security. The study in artificial intelligence (AI) and BT is widely popular due to its expanding importance in enhancing security and computational prowess. In this study, we present a comprehensive and meticulous comprehensive review of AI and BT‐based privacy‐preserving smart healthcare. The selection for this study was based on a holistic and integrated approach which involved examining not only individual studies but also their relationships, and trends. Through a systematic analysis of various phases, we identified 91 primary studies pertaining to information needed to stockpile directions called for retorting the research queries. We have undertaken a descriptive comparison of foundational manuscripts, taking into account an array of essential factors, including performance metrics, security protocols, and computational prowess. Our thorough discussions and debates have led to the identification of research gaps in the current manuscript, as well as the direction for future research. We also propose our constructive approach for the aforementioned integration, highlighting its potential benefits and implications.

Neelesh Mungoli

This paper explores the potential of using ChatGPT, a state-of-the-art conversational AI system, to enhance engagement and understanding of cryptocurrency. We first provide a comprehensive review of the existing literature on both cryptocurrency and ChatGPT. We then describe the background of cryptocurrency and the capabilities of ChatGPT. We present our methodology for collecting and preprocessing a dataset of cryptocurrency-related conversations and fine-tuning ChatGPT using reinforcement learning. Our results demonstrate the effectiveness of ChatGPT in generating contextually appropriate responses to cryptocurrency-related queries, with potential applications in areas such as customer support and education. However, we also identify challenges and limitations associated with the deployment of ChatGPT in this domain, including the need for robust data privacy measures and addressing potential biases. Our findings suggest promising directions for future research in enhancing conversational engagement and understanding of cryptocurrency through ChatGPT

Junyu Ren, Tuanfa Qin

Although smart healthcare has achieved rapid development and has gained considerable attention from academia and industry, pressing challenges exist, such as resource limitation and stringent security demand. To tackle the issues, a blockchain-based and trust-aware task-offloading strategy (BBTAS) is proposed in this work, whereby task-offloading requests are published as blockchain transactions that are automatically yet securely performed by smart contracts (SCs). An SC is a set of codes implementing predefined rules agreed by the participants involved. When a rule meets certain criteria or triggered by some events or transactions, an SC can perform by itself and generate verifiable results, which can be validated and securely stored in the blockchain. The proposed recommendation filtering method (RFM) and trust penalty measure (TPM)-based trust mechanism is powerful in resisting network insider attacks, and the inherent time-inefficiency problem of blockchain can be alleviated by integrating trust factor, which is theoretically analyzed in this work. To optimize the selection of service node, a utility-based decision-making method (UB-DMM) is proposed, which can maximize the system utility on the basis of fully considering multiple significant performance metrics of the system. Extensive simulations have been carried out to validate the effectiveness and superiority of the proposed measures.

Zahra Mohammadzadeh, Marcel Ausloos, Hamid Reza Saeidnia

Purpose ChatGPT from OpenAI is an amazing example of machine learning technology. This technology has now become an important issue for high-tech plagiarism concern. Indeed, there are many concerns about using this tool, perhaps using other technologies to make ChatGPT safer. Non-fungible tokens (NFTs) may be a way out. This paper aims to discuss such an alternative. Design/methodology/approach To preventing with high-tech plagiarism created by the ChatGPT tool two ways can help schools, universities and scientific centers to prevent academic plagiarism: first, by banning ChatGPT and adjusting teaching styles, and second, by using detecting AI-produced content. In this viewpoint, the authors suggest a third way that can be a way out. Findings NFTs technology has the ability to add a non-fungibility feature to any digital object (image, text or video). Therefore, any text produced by artificial intelligence tools can be given a specific NFT code. With this work, the authors add a feature to texts produced by artificial intelligence, that is, the non-fungibility feature. Originality/value In this viewpoint, how and why NFTs may be a usefully added value in preventing acts of high-tech plagiarism on ChatGPT is discussed.

C. U. Om Kumar, Sudhakaran Gajendran, Viswaksena Balaji, A. Nhaveen · 5 authors

Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.

Zongwei Li, Dechao Kong, Yuanzheng Niu, Hong-Li Peng · 6 authors

With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecting privacy of individuals, these techniques also guarantee security and dependability of data. This paper initially presents an overview of AI and blockchain, summarizing their combination along with derived privacy protection technologies. It then explores specific application scenarios in data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity methods. Moreover, the paper evaluates five critical aspects of AI-blockchain-integration privacy protection systems, including authorization management, access control, data protection, network security, and scalability. Furthermore, it analyzes the deficiencies and their actual cause, offering corresponding suggestions. This research also classifies and summarizes privacy protection techniques based on AI-blockchain application scenarios and technical schemes. In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve a more comprehensive privacy protection of privacy.

Madhuri Hiwale, Rahee Walambe, Vidyasagar Potdar, Ketan Kotecha

The unexpected and rapid spread of the COVID-19 pandemic has amplified the acceptance of remote healthcare systems such as telemedicine. Telemedicine effectively provides remote communication, better treatment recommendation, and personalized treatment on demand. It has emerged as the possible future of medicine. From a privacy perspective, secure storage, preservation, and controlled access to health data with consent are the main challenges to the effective deployment of telemedicine. It is paramount to fully overcome these challenges to integrate the telemedicine system into healthcare. In this regard, emerging technologies such as blockchain and federated learning have enormous potential to strengthen the telemedicine system. These technologies help enhance the overall healthcare standard when applied in an integrated way. The primary aim of this study is to perform a systematic literature review of previous research on privacy-preserving methods deployed with blockchain and federated learning for telemedicine. This study provides an in-depth qualitative analysis of relevant studies based on the architecture, privacy mechanisms, and machine learning methods used for data storage, access, and analytics. The survey allows the integration of blockchain and federated learning technologies with suitable privacy techniques to design a secure, trustworthy, and accurate telemedicine model with a privacy guarantee.