CloudML: Privacy-Assured Healthcare Machine Learning Model for Cloud Network

Title

CloudML: Privacy-Assured Healthcare Machine Learning Model for Cloud Network

Creator

Savitha S.; Ravichandran S.K.

Description

Cloud computing is the need of the twenty-first century with an exponential increase in the volume of data. Compared to any other technologies, the cloud has seen fastest adoption in the industry. The popularity of cloud is closely linked to the benefits it offers which ranges from a group of stakeholders to huge number of entrepreneurs. This enables some prominent features such as elasticity, scalability, high availability, and accessibility. So, the increase in popularity of the cloud is linked to the influx of data that involves big data with some specialized techniques and tools. Many data analysis applications use clustering techniques incorporated with machine learning to derive useful information by grouping similar data, especially in healthcare and medical department for predicting symptoms of diseases. However, the security of healthcare data with a machine learning model for classifying patients information and genetic data is a major concern. So, to solve such problems, this paper proposes a Cloud-Machine Learning (CloudML) Model for encrypted heart disease datasets by employing a privacy preservation scheme in it. This model is designed in such a way that it does not vary in accuracy while clustering the datasets. The performance analysis of the model shows that the proposed approach yields significant results in terms of Communication Overhead, Storage Overhead, Runtime, Scalability, and Encryption Cost. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-213, pp. 51-64.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Cloud privacy; Clustering; Encryption; Healthcare datasets; Machine learning; Network performance

Coverage

Savitha S., Department of Computer Science and Engineering, Christ University, Bangalore, India; Ravichandran S.K., Department of Computer Science and Engineering, Christ University, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981162421-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-16-2422-3_5

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115172237&doi=10.1007%2f978-981-16-2422-3_5&partnerID=40&md5=df2db7df7e45b9f22f7a937c73565bf3