Improving cloud storage and privacy security for digital twin based medical records
As digital transformation progresses across industries, digital twins have emerged as an important technology. In healthcare, digital twins are created by digitizing patient parameters, medical records, and treatment plans to enable personalized care, assist diagnosis, and improve planning. Data is...
Gespeichert in:
Veröffentlicht in: | Journal of Cloud Computing 2023-12, Vol.12 (1), p.151-16, Article 151 |
---|---|
1. Verfasser: | |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | As digital transformation progresses across industries, digital twins have emerged as an important technology. In healthcare, digital twins are created by digitizing patient parameters, medical records, and treatment plans to enable personalized care, assist diagnosis, and improve planning. Data is core to digital twins, originating from physical and virtual entities as well as services. Once processed and integrated, data drives various components. Medical records are critical healthcare data but present unique challenges for digital twins. However, directly storing or encrypting medical records has issues. Plaintext risks privacy leaks while encryption hinders retrieval. To address this, we present a cloud-based solution combining post-quantum searchable encryption. Our system includes key generation using Physical Unable Functions (PUF). It encrypts medical records in cloud storage, verifies records using blockchain, and retrieves records via cloud. By integrating cloud encryption, blockchain verification and cloud retrieval, we propose a secure and efficient cloud-based medical records system for digital twins. Our implementation demonstrates the system provides users efficient and secure medical record services, compared to related designs. This highlights digital twins’ potential to transform healthcare through secure data-driven personalized care, diagnosis and planning. |
---|---|
ISSN: | 2192-113X 2192-113X |
DOI: | 10.1186/s13677-023-00523-6 |