A Dual-Chain and Differential Privacy-Based Solution for Medical Data Privacy Protection and Access Control

Abstract: As living standards rise, people are paying increasing attention to health. Vast quantities of medical data are generated daily, yet each piece contains sensitive information such as patients’ names, mobile numbers, email addresses, and places of employment. Should this information be compromised, the consequences would be irreversible, causing severe damage. Traditional solutions merely implement access control policies, permitting data access only to authorised personnel. While this approach offers some protection, even compliant users cannot be entirely trusted and may engage in malicious activities. Once data is accessed, patients’ sensitive information becomes fully exposed to the user, posing a significant data security risk. Therefore, this study proposes a medical data sharing scheme based on Dual-Chain and differential privacy. It employs a hybrid approach combining private chains, consortium chains, and IPFS. Internal hospital personnel can access data after de-identification, while external parties can only access data that has been de-identified and subsequently augmented with noise. This significantly enhances security. The experimental section of this study also demonstrates that the proposed scheme effectively protects data, while the data shared with external users enables them to successfully complete downstream tasks.

Cen Gu, Luping Wang and Hongjie Wu. “A Dual-Chain and Differential Privacy-Based Solution for Medical Data Privacy Protection and Access Control”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.2 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170288

@article{Gu2026,
title = {A Dual-Chain and Differential Privacy-Based Solution for Medical Data Privacy Protection and Access Control},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170288},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170288},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {2},
author = {Cen Gu and Luping Wang and Hongjie Wu}
}