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A Comprehensive Analysis of Privacy-preserving Techniques in Deep learning based Disease Prediction Systems by J Andrew; Shaun Shibu Mathew; Batra Mohit is a Physics and Astronomy article available to read on EtoBox.

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## Abstract With the rise in demand for deep learning models due to its ability to learn features from data, and predict, it is widely used in disease prediction systems. However, as patient medical records are considered to be highly confidential due to them consisting of personal information, its privacy-preservation is of prime importance. Conventional privacy-preserving techniques often tend to hinder the utilitarian aspect of the system. In this paper we carry out a comprehensive analysis of privacy-preserving techniques for disease prediction systems that use deep learning along with a comparison of the different privacy-preserving techniques. This paper also discusses the existing privacy-preserving approaches in deep learning. They are cryptographic approaches, attribute-based encryptions, homomorphic encryptions and other hybrid approaches.

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Author

J Andrew; Shaun Shibu Mathew; Batra Mohit

Publisher

Institute of Physics; IOP Publishing (ISSN 1742-6588)

Published

2019

Language

EN

Field

Physics and Astronomy (Physical Sciences)