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Castro C, Leiva V, Garrido D, Huerta M, Minatogawa V. Blockchain in clinical trials: Bibliometric and network studies of applications, challenges, and future prospects based on data analytics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108321. [PMID: 39053350 DOI: 10.1016/j.cmpb.2024.108321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/14/2024] [Accepted: 07/07/2024] [Indexed: 07/27/2024]
Abstract
This study conducts a comprehensive analysis on the usage of the blockchain technology in clinical trials, based on a curated corpus of 107 scientific articles from the year 2016 through the first quarter of 2024. Utilizing a methodological framework that integrates bibliometric analysis, network analysis, thematic mapping, and latent Dirichlet allocation, the study explores the terrain and prospective developments within this usage based on data analytics. Through a meticulous examination of the analyzed articles, the present study identifies seven key thematic areas, highlighting the diverse applications and interdisciplinary nature of blockchain in clinical trials. Our findings reveal blockchain capability to enhance data management, participant consent processes, as well as overall trial transparency, efficiency, and security. Additionally, the investigation discloses the emerging synergy between blockchain and advanced technologies, such as artificial intelligence and federated learning, proposing innovative directions for improving clinical research methodologies. Our study underscores the collaborative efforts in dealing with the complexities of integrating blockchain into the areas of clinical trials and healthcare, delineating the transformative potential of blockchain technology in revolutionizing these areas by addressing challenges and promoting practices of efficient, secure, and transparent research. The delineated themes and networks of collaboration provide a blueprint for future inquiry, showing the importance of empirical research to narrow the gap between theoretical promise and practical implementation.
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Affiliation(s)
- Cecilia Castro
- Centre of Mathematics, Universidade do Minho, Braga, Portugal
| | - Víctor Leiva
- Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
| | - Diego Garrido
- Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Mauricio Huerta
- Escuela de Ingeniería Industrial, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Vinicius Minatogawa
- Escuela de Ingeniería en Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
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2
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Teo ZL, Quek CWN, Wong JLY, Ting DSW. Cybersecurity in the generative artificial intelligence era. Asia Pac J Ophthalmol (Phila) 2024; 13:100091. [PMID: 39209217 DOI: 10.1016/j.apjo.2024.100091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/29/2024] [Accepted: 08/08/2024] [Indexed: 09/04/2024] Open
Abstract
Generative Artificial Intelligence (GenAI) are algorithms capable of generating original content. The ability of GenAI to learn and generate novel outputs alike human cognition has taken the world by storm and ushered in a new era. In this review, we explore the role of GenAI in healthcare, including clinical, operational, and research applications, and delve into the cybersecurity risks of this technology. We discuss risks such as data privacy risks, data poisoning attacks, the propagation of bias, and hallucinations. In this review, we recommend risk mitigation strategies to enhance cybersecurity in GenAI technologies and further explore the use of GenAI as a tool in itself to enhance cybersecurity across the various AI algorithms. GenAI is emerging as a pivotal catalyst across various industries including the healthcare domain. Comprehending the intricacies of this technology and its potential risks will be imperative for us to fully capitalise on the benefits that GenAI can bring.
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Affiliation(s)
- Zhen Ling Teo
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore.
| | - Chrystie Wan Ning Quek
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Duke-NUS Medical School Singapore, Singapore
| | - Joy Le Yi Wong
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Duke-NUS Medical School Singapore, Singapore
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Duke-NUS Medical School Singapore, Singapore.
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Zhao L, Yu X, Zhou X. Regulatory mechanism of vulnerability disclosure behavior considering security crowd-testing: An evolutionary game analysis. PLoS One 2024; 19:e0304467. [PMID: 38905256 PMCID: PMC11192317 DOI: 10.1371/journal.pone.0304467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 05/10/2024] [Indexed: 06/23/2024] Open
Abstract
The security crowd-testing regulatory mechanism is a vital means to promote collaborative vulnerability disclosure. However, existing regulatory mechanisms have not considered multi-agent responsibility boundaries and stakeholders' conflicts of interest, leading to their dysfunction. Distinguishing from previous research on the motivations and constraints of ethical hacks' vulnerability disclosure behaviors from a legal perspective, this paper constructs an evolutionary game model of SRCs, security researchers, and the government from a managerial perspective to propose regulatory mechanisms promoting tripartite collaborative vulnerability disclosure. The results show that the higher the initial willingness of the three parties to choose the collaborative strategy, the faster the system evolves into a stable state. Regarding the government's incentive mechanism, establishing reward and punishment mechanisms based on effective thresholds is essential. However, it is worth noting that the government has an incentive to adopt such mechanisms only if it receives sufficient regulatory benefits. To further facilitate collaborative disclosure, Security Response Centers (SRC) should establish incentive mechanisms including punishment and trust mechanisms. Additionally, publicity and training mechanisms for security researchers should be introduced to reduce their revenue from illegal participation, which promotes the healthy development of security crowd-testing. These findings contribute to improving SRCs' service quality, guiding security researchers' legal participation, enhancing the government's regulatory effectiveness, and ultimately establishing a multi-party collaborative vulnerability disclosure system.
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Affiliation(s)
- Liurong Zhao
- School of Economics and Management, Nanjing Tech University, Nanjing, Jiangsu, China
| | - Xiaoxi Yu
- School of Economics and Management, Nanjing Tech University, Nanjing, Jiangsu, China
| | - Xinyu Zhou
- School of Economics and Management, Nanjing Tech University, Nanjing, Jiangsu, China
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SaberiKamarposhti M, Ng KW, Chua FF, Abdullah J, Yadollahi M, Moradi M, Ahmadpour S. Post-quantum healthcare: A roadmap for cybersecurity resilience in medical data. Heliyon 2024; 10:e31406. [PMID: 38826742 PMCID: PMC11141384 DOI: 10.1016/j.heliyon.2024.e31406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 05/02/2024] [Accepted: 05/15/2024] [Indexed: 06/04/2024] Open
Abstract
As healthcare systems transition into an era dominated by quantum technologies, the need to fortify cybersecurity measures to protect sensitive medical data becomes increasingly imperative. This paper navigates the intricate landscape of post-quantum cryptographic approaches and emerging threats specific to the healthcare sector. Delving into encryption protocols such as lattice-based, code-based, hash-based, and multivariate polynomial cryptography, the paper addresses challenges in adoption and compatibility within healthcare systems. The exploration of potential threats posed by quantum attacks and vulnerabilities in existing encryption standards underscores the urgency of a change in basic assumptions in healthcare data security. The paper provides a detailed roadmap for implementing post-quantum cybersecurity solutions, considering the unique challenges faced by healthcare organizations, including integration issues, budget constraints, and the need for specialized training. Finally, the abstract concludes with an emphasis on the importance of timely adoption of post-quantum strategies to ensure the resilience of healthcare data in the face of evolving threats. This roadmap not only offers practical insights into securing medical data but also serves as a guide for future directions in the dynamic landscape of post-quantum healthcare cybersecurity.
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Affiliation(s)
- Morteza SaberiKamarposhti
- Faculty of Computing and Informatics (FCI), Multimedia University (MMU), 63100, Cyberjaya, Selangor, Malaysia
| | - Kok-Why Ng
- Faculty of Computing and Informatics (FCI), Multimedia University (MMU), 63100, Cyberjaya, Selangor, Malaysia
| | - Fang-Fang Chua
- Faculty of Computing and Informatics (FCI), Multimedia University (MMU), 63100, Cyberjaya, Selangor, Malaysia
| | - Junaidi Abdullah
- Faculty of Computing and Informatics (FCI), Multimedia University (MMU), 63100, Cyberjaya, Selangor, Malaysia
| | - Mehdi Yadollahi
- Faculty of Computer Engineering, Islamic Azad University, Ayatollah Amoli Branch, Amol, Iran
| | - Mona Moradi
- Department of Computer Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
| | - Sima Ahmadpour
- Graduate School of Business, Universiti Sains Malaysia, 11800, USM, Pulau Pinang, Malaysia
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Khan HU, Ali Y. Modeling security evaluation framework for IoHT-driven systems using integrated decision-making methodology. Sci Rep 2024; 14:12233. [PMID: 38806575 PMCID: PMC11133348 DOI: 10.1038/s41598-024-62066-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/13/2024] [Indexed: 05/30/2024] Open
Abstract
The intensification of the Internet of Health Things devices created security concerns due to the limitations of these devices and the nature of the healthcare data. While dealing with the security challenges, several authentication schemes, protocols, processes, and standards have been adopted. Consequently, making the right decision regarding the installation of a secure authentication solution or procedure becomes tricky and challenging due to the large number of security protocols, complexity, and lack of understanding. The major objective of this study is to propose an IoHT-based assessment framework for evaluating and prioritizing authentication schemes in the healthcare domain. Initially, in the proposed work, the security issues related to authentication are collected from the literature and consulting experts' groups. In the second step, features of various authentication schemes are collected under the supervision of an Internet of Things security expert using the Delphi approach. The collected features are used to design suitable criteria for assessment and then Graph Theory and Matrix approach applies for the evaluation of authentication alternatives. Finally, the proposed framework is tested and validated to ensure the results are consistent and accurate by using other multi-criteria decision-making methods. The framework produces promising results such as 93%, 94%, and 95% for precision, accuracy, and recall, respectively in comparison to the existing approaches in this area. The proposed framework can be picked as a guideline by healthcare security experts and stakeholders for the evaluation and decision-making related to authentication issues in IoHT systems.
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Affiliation(s)
- Habib Ullah Khan
- Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar.
| | - Yasir Ali
- Shahzeb Shaheed Government Degree College Razzar, Swabi, Higher Education Department, Peshawar Khyber Pakhtunkhwa, Pakistan
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Nandagopal M, Seerangan K, Govindaraju T, Abi NE, Balusamy B, Selvarajan S. A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems. Sci Rep 2024; 14:10280. [PMID: 38704423 PMCID: PMC11069552 DOI: 10.1038/s41598-024-59846-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 04/16/2024] [Indexed: 05/06/2024] Open
Abstract
In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients' medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier's error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL's effectiveness and efficiency in identifying diseases is evaluated and compared.
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Affiliation(s)
- Malarvizhi Nandagopal
- Department of CSE, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, 600062, India
| | - Koteeswaran Seerangan
- Department of CSE (AI&ML), S.A. Engineering College (Autonomous), Chennai, Tamil Nadu, 600077, India
| | - Tamilmani Govindaraju
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, 603203, India
| | - Neeba Eralil Abi
- Department of Information Technology, Rajagiri School of Engineering and Technology, Kochi, Kerala, 682039, India
| | - Balamurugan Balusamy
- Shiv Nadar (Institution of Eminence Deemed to be University), Greater Noida, Uttar Pradesh, 201314, India
| | - Shitharth Selvarajan
- Department of Computer Science, Kebri Dehar University, 250, Kebri Dehar, Ethiopia.
- School of Built Environment, Engineering and Computing, Leeds Beckett University, LS1 3HE, Leeds, UK.
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Haque EU, Shah A, Iqbal J, Ullah SS, Alroobaea R, Hussain S. A scalable blockchain based framework for efficient IoT data management using lightweight consensus. Sci Rep 2024; 14:7841. [PMID: 38570648 PMCID: PMC10991409 DOI: 10.1038/s41598-024-58578-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 04/01/2024] [Indexed: 04/05/2024] Open
Abstract
Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required.
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Affiliation(s)
- Ehtisham Ul Haque
- Department of Computer Science, MY University, Islamabad, 44000, Pakistan
| | - Adil Shah
- Department of Computer Science, MY University, Islamabad, 44000, Pakistan
| | - Jawaid Iqbal
- Faculty of Computing, Riphah International University, Islamabad, 45320, Pakistan
| | - Syed Sajid Ullah
- Department of Information and Communication Technology, University of Agder (UiA), N-4898, Grimstad, Norway.
| | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
| | - Saddam Hussain
- School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE1410, Brunei
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Syed SA, Manickam S, Uddin M, Alsufyani H, Shorfuzzaman M, Selvarajan S, Mohammed GB. Dickson polynomial-based secure group authentication scheme for Internet of Things. Sci Rep 2024; 14:4947. [PMID: 38418484 PMCID: PMC10902399 DOI: 10.1038/s41598-024-55044-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 02/20/2024] [Indexed: 03/01/2024] Open
Abstract
Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli's Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.
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Affiliation(s)
- Salman Ali Syed
- Department of Computer Science, Applied College Tabarjal, Jouf University, Sakaka, Al-Jouf Province, Kingdom of Saudi Arabia
| | - Selvakumar Manickam
- National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, 11800, Gelugor, Penang, Malaysia
| | - Mueen Uddin
- College of Computing and IT, University of Doha for Science and Technology, 24449, Doha, Qatar
| | - Hamed Alsufyani
- Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, 11673, Riyadh, Kingdom of Saudi Arabia
| | - Mohammad Shorfuzzaman
- Department of Computer Science, College of Computers and Information Technology, Taif University, 21944, Taif, Kingdom of Saudi Arabia
| | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS1 3HE, UK.
- Department of Computer Science and Engineering, Kebri Dehar University, 250, Kebri Dehar, Ethiopia.
| | - Gouse Baig Mohammed
- Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, India
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Ali G, ElAffendi M, Ahmad N. BlockAuth: A blockchain-based framework for secure vehicle authentication and authorization. PLoS One 2023; 18:e0291596. [PMID: 37733686 PMCID: PMC10513222 DOI: 10.1371/journal.pone.0291596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 09/04/2023] [Indexed: 09/23/2023] Open
Abstract
Intelligent Transport System (ITS) offers inter-vehicle communication, safe driving, road condition updates, and intelligent traffic management. This research intends to propose a novel decentralized "BlockAuth" architecture for vehicles, authentication, and authorization, traveling across the border. It is required because the existing architects rely on a single Trusted Authority (TA) for issuing certifications, which can jeopardize privacy and system integrity. Similarly, the centralized TA, if failed, can cause the whole system to collapse. Furthermore, a unique "Proof of Authenticity and Integrity" process is proposed, redirecting drivers/vehicles to their home country for authentication, ensuring the security of their credentials. Implemented with Hyperledger Fabric, BlockAuth ensures secure vehicle authentication and authorization with minimal computational overhead, under 2%. Furthermore, it opens up global access, enforces the principles of separation of duty and least privilege, and reinforces resilience via decentralization and automation.
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Affiliation(s)
- Gauhar Ali
- EIAS Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
| | - Mohammed ElAffendi
- EIAS Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
| | - Naveed Ahmad
- EIAS Data Science and Blockchain Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
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