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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.16411136 |
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| _version_ | 1866901741360906240 |
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| author | Hasibullah Begzada |
| author_facet | Hasibullah Begzada |
| contents | <p>The integration of Internet of Things (IoT) devices and cloud computing has revolutionized healthcare data management by enabling real-time data collection, storage, and analysis. However, ensuring the security and privacy of sensitive healthcare<br>data remains a major challenge. This study proposes a secure healthcare data management framework that utilizes Advanced<br>Encryption Standard (AES) encryption for data protection while leveraging cloud computing for scalable storage. Experimental<br>results demonstrate that encryption time increases linearly with data size, highlighting performance considerations for large<br>datasets. Additionally, storage latency trends emphasize the need for optimized data retrieval strategies. The proposed framework enhances the security, accessibility, and reliability of healthcare data while maintaining compliance with privacy regulations. Future enhancements may focus on improving encryption efficiency and integrating machine learning for anomaly detection in healthcare data security</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16411136 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Secure Healthcare Data Management Using IOT, Cloud Computing and Encryption Hasibullah Begzada <p>The integration of Internet of Things (IoT) devices and cloud computing has revolutionized healthcare data management by enabling real-time data collection, storage, and analysis. However, ensuring the security and privacy of sensitive healthcare<br>data remains a major challenge. This study proposes a secure healthcare data management framework that utilizes Advanced<br>Encryption Standard (AES) encryption for data protection while leveraging cloud computing for scalable storage. Experimental<br>results demonstrate that encryption time increases linearly with data size, highlighting performance considerations for large<br>datasets. Additionally, storage latency trends emphasize the need for optimized data retrieval strategies. The proposed framework enhances the security, accessibility, and reliability of healthcare data while maintaining compliance with privacy regulations. Future enhancements may focus on improving encryption efficiency and integrating machine learning for anomaly detection in healthcare data security</p> |
| title | Secure Healthcare Data Management Using IOT, Cloud Computing and Encryption |
| url | https://doi.org/10.5281/zenodo.16411136 |