Differential and TriPhase Adaptive Learning-Based Privacy-Preserving Model for Medical Data in Cloud Environment

This letter proposes a novel secure data protection model for privacy preservation in the cloud environment by partitioning, sanitizing, and analyzing the data effectively to improve the model’s privacy. Several experiments and comparisons of the proposed model with existing works indicate that it protects data with high accuracy, precision, recall, and F1-score up to 87.03%, 84.87%, 87.03%, and 85.00%, for diverse datasets with a relative improvement up to 15.89%, 27.73%, 15.89%, and 21.44%, respectively.

I. Introduction

Data sharing and analysis is essential for pathology centers to enhance the utility and quality of medical services. Most of the centres have shifted or planned to shift their data to the cloud platform because of its storage and analysis services at lower cost [1]. The outsourced data can be accessed by researchers or laboratories to monitor and track the spread of infectious diseases. The centers are hesitant to share their sensitive data to the cloud or any third party since these may misuse and leak the data to other parties without owners’ consent [2]. Thus, protecting shared data across the entities while performing analysis effectively has become one of the most challenging problems in cloud environments.