Differentially private model release for healthcare applications
S. Sangeetha, G. Sudha Sadasivam, Ayush Srikanth · International Journal of Computers and Applications · 2022
The evolution of technology allowed the collection of a large amount of user data, known as big data. Among all the datasets healthcare data is more sensitive. It is extremely important to protect the individual users in such datasets. Even anonymized data release is vulnerable. Hence, in this paper, we suggest a differential privacy-based model release instead of the data release. A private model release based on six machine learning classifiers namely Support Vector Machine (SVM), Random Forest algorithm, Logistic Regression, K-Nearest Neighbor, Decision Tree, and Naive Bayes are proposed. Experimental evaluation is performed using the benchmark heart disease dataset and the accuracy of the model is analyzed. The published private model can be used for the prediction of possible heart disease in patients.