Differentially private model release for healthcare applications

被引：1

作者：

Sangeetha S. ^{[1
]}

Sudha Sadasivam G. ^{[2
]}

Srikanth A. ^{[1
]}

机构：

[1] Department of Information Technology, PSG College of Technology, Tamil Nadu, Coimbatore

[2] Department of Computer Science and Engineering, PSG College of Technology, Tamil Nadu, Coimbatore

来源：

International Journal of Computers and Applications | 2022年 / 44卷 / 10期

关键词：

Differential privacy; health care; machine learning; model release; privacy preserved learning; private model;

D O I：

10.1080/1206212X.2021.2024958

中图分类号：

学科分类号：

摘要：

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. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

引用

页码：953 / 958

页数：5

共 29 条

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