Enhancing Secure Cloud Storage for Synthetic Healthcare Data: Encryption and Scalable Privacy-Preserving Analysis

Abstract

With the heightened reliance on cloud computing for storage and management of sensitive health care data, the aspects of data security and privacy have become a real focus. This paper proposes improvements on a cloud storage setting for synthetic healthcare data, enhanced by embedding Modified Identity-Based Encryption (MIBE) into the security of the patient before uploading to cloud storage. The proposed system provides scalability, privacy-preserving analysis, and compliance with healthcare laws. In order to treat missing values and create a standardized dataset, we introduce pre-processing techniques such as k-NN Imputation and Z-Score Method. With the MIBE encryption mechanism, the encryption key would be tied to the identity of the user; hence it would be a strong method to secure the data while reducing the burden of managing keys. The results show a linear increment in encryption time concerning the number of files, which took 2.23 seconds for 100 files and 19.02 seconds for 1000, proving the efficiency of the encryption mechanism. After performing 10 operations, the proposed model achieved a security score of 9.8 units, which validates the scalability of the proposed setup. This work gives insight into a secure setup for managing healthcare data on cloud platforms with proven gain on scalability. This addresses, therefore, an emerging concern on the data security and privacy of modern-day health care systems.