Edge-DPSDG: An Edge-Based Differential Privacy Protection Model for Smart Healthcare

Abstract

The edge computing paradigm has revolutionized the healthcare sector, providing more real-time medical data processing and analysis, which also poses more serious privacy and security risks that must be carefully considered and addressed. Based on differential privacy, we presented an innovative privacy-preserving model named Edge-DPSDG (Edge-Differentially Private Synthetic Data Generator) for smart healthcare under edge computing. It also develops and evolves a privacy budget allocation mechanism. In a distributed environment, the privacy budget for local medical data is personalized by computing the Shapley value and the information entropy value of each attribute in the dataset, which takes into account the trade-off between data privacy and utility. Extensive experiments on three public medical datasets are performed to evaluate the performance of Edge-DPSDG on two metrics. For utility evaluation, Edge-DPSDG shows a best 21.29% accuracy improvement compared to the state-of-the-art; our privacy budget allocation mechanism improved existing models’ accuracy by up to 6.05%. For privacy evaluation, Edge-DPSDG shows that can effectively ensure the privacy of the original datasets. In addition, Edge-DPSDG helps smooth the data, and results in a 3.99% accuracy loss decrease over the non-private model.