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<p>Blockchain technology offers a secure, transparent method for data transmission by decentralizing healthcare data management. When integrated with Personalized Digital Twins (PDTs), it enables personalized e-healthcare while ensuring the protection of sensitive health information through advanced privacy and security mechanisms. This study explores how blockchain can enhance data transmission security in personalized e-healthcare systems, particularly in the context of human digital twins, to improve individual well-being and safeguard health data privacy. The study investigates the role of PDTs in optimizing patient decision-making and treatment planning. By replicating a physical patient model in a virtual environment, the PDT processes sensor data to guide healthcare decisions. To ensure top-tier service quality, especially in Internet of Things (IoT) environments, the research proposes a permissioned blockchain with a Proof of Authority (PoA) trust model. This mechanism enhances security and privacy for sensitive health data, ensuring that only authorized entities can access or modify it. The study also incorporates Deterministic Pseudorandom Generation (DPRG) for generating the initial block in the blockchain, adding an extra layer of decentralization and reliability. An innovative end-to-end framework, combining Non-Negative Matrix Factorization (NMF) and a Product-based Neural Network (PNN), integrates the PDT system with Digital Twin configurations to enhance healthcare delivery. A vulnerability-tolerant design is adopted for the Digital Twins (DTs), with a Keras Deep Residual Neural Network (KDRNN) used to detect cybersecurity threats and anomalies. Smart contracts are employed to verify the authenticity of DTs and Personal Assistants (PAs), preventing malicious interference in data synchronization. Findings from the study show that, with each sensing instance, a delay of 0.260&#xa0;s occurred, implemented in Python Software. Higher sensing frequency led to faster decision-making, improving computational efficiency and enabling real-time data transfer. The study provides a strong foundation for building more secure and efficient healthcare systems in the digital age.</p>

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Blockchain-enabled secure data transmission for personalized e-healthcare and digital twin well-being

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • Ashutosh Shankhdhar,
  • Hitendra Garg

摘要

Blockchain technology offers a secure, transparent method for data transmission by decentralizing healthcare data management. When integrated with Personalized Digital Twins (PDTs), it enables personalized e-healthcare while ensuring the protection of sensitive health information through advanced privacy and security mechanisms. This study explores how blockchain can enhance data transmission security in personalized e-healthcare systems, particularly in the context of human digital twins, to improve individual well-being and safeguard health data privacy. The study investigates the role of PDTs in optimizing patient decision-making and treatment planning. By replicating a physical patient model in a virtual environment, the PDT processes sensor data to guide healthcare decisions. To ensure top-tier service quality, especially in Internet of Things (IoT) environments, the research proposes a permissioned blockchain with a Proof of Authority (PoA) trust model. This mechanism enhances security and privacy for sensitive health data, ensuring that only authorized entities can access or modify it. The study also incorporates Deterministic Pseudorandom Generation (DPRG) for generating the initial block in the blockchain, adding an extra layer of decentralization and reliability. An innovative end-to-end framework, combining Non-Negative Matrix Factorization (NMF) and a Product-based Neural Network (PNN), integrates the PDT system with Digital Twin configurations to enhance healthcare delivery. A vulnerability-tolerant design is adopted for the Digital Twins (DTs), with a Keras Deep Residual Neural Network (KDRNN) used to detect cybersecurity threats and anomalies. Smart contracts are employed to verify the authenticity of DTs and Personal Assistants (PAs), preventing malicious interference in data synchronization. Findings from the study show that, with each sensing instance, a delay of 0.260 s occurred, implemented in Python Software. Higher sensing frequency led to faster decision-making, improving computational efficiency and enabling real-time data transfer. The study provides a strong foundation for building more secure and efficient healthcare systems in the digital age.