Privacy Preserving Machine Learning and Deep Learning Techniques: Application – E-Healthcare

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Author(s): Divya Asok (Thiagarajar College of Engineering, India), Chitra P. (Thiagarajar College of Engineering, India)and Bharathiraja Muthurajan (Indian Institute of Technology Madras, India)
Copyright: 2021
Pages: 14
Source title: Research Anthology on Privatizing and Securing Data
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-8954-0.ch078

Keywords: Cyber and Network Security / Information Science Reference / Information Security & Privacy / Security & Forensics

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Abstract

In the past years, the usage of internet and quantity of digital data generated by large organizations, firms, and governments have paved the way for the researchers to focus on security issues of private data. This collected data is usually related to a definite necessity. For example, in the medical field, health record systems are used for the exchange of medical data. In addition to services based on users' current location, many potential services rely on users' location history or their spatial-temporal provenance. However, most of the collected data contain data identifying individual which is sensitive. With the increase of machine learning applications around every corner of the society, it could significantly contribute to the preservation of privacy of both individuals and institutions. This chapter gives a wider perspective on the current literature on privacy ML and deep learning techniques, along with the non-cryptographic differential privacy approach for ensuring sensitive data privacy.

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Privacy Preserving Machine Learning and Deep Learning Techniques: Application – E-Healthcare

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