A novel secure privacy-preserving data sharing model with deep-based key generation on the blockchain network in the cloud
Kasturi, K and Samuel, B (2025) A novel secure privacy-preserving data sharing model with deep-based key generation on the blockchain network in the cloud. Computer Standards & Interfaces, 92. pp. 1-17. ISSN 09205489
Full text not available from this repository. (Request a copy)Abstract
Combining Artificial Intelligence (AI) and the Internet of Things (IoT) into AIoT has significantly transformed healthcare. This study explores AIoT’s role in health monitoring and diagnostics, highlighting its potential to revolutionize patient care. Utilizing wearables, remote monitoring, and predictive diagnostics, AIoT provides personalized and proactive healthcare solutions. It shifts healthcare from reactive to anticipatory by offering professionals predictive insights from patient data and advanced analytics. This enhances adaptability, effectiveness, and patient-focused care, addressing the limitations of traditional methods. However, ethical issues such as patient autonomy and data privacy must be considered as AIoT advances. Responsible implementation is essential to protect patient rights while leveraging AIoT’s benefits. Wearable technology and remote monitoring enable seamless data collection, real-time tracking, and informed decision-making, promoting active patient engagement and accurate interventions. As AIoT continues to evolve, establishing ethical guidelines and regulations is crucial. Overall, AIoT holds great promise for transforming healthcare delivery, improving patient outcomes, and shaping the future of medical practice when implemented ethically.
| Item Type: | Article |
|---|---|
| Subjects: | Computer Applications > Technology Computer Applications > Cloud Computing Computer Applications > Computer Architecture |
| Domains: | Computer Applications |
| Depositing User: | Mr IR Admin |
| Date Deposited: | 08 Aug 2025 06:41 |
| Last Modified: | 11 May 2026 07:08 |
| URI: | https://ir.vistas.ac.in/id/eprint/9881 |
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