research-article

An efficient perturbation approach for multivariate data in sensitive and reliable data mining

Authors:

Mahit Kumar Paul,

Md. Rabiul Islam,

A.H.M. Sarowar SattarAuthors Info & Claims

Volume 62, Issue C

https://doi.org/10.1016/j.jisa.2021.102954

Published: 01 November 2021 Publication History

Abstract

Due to the rapid enhancement of technology, cloud data is increasing rapidly which contains individuals’ sensitive information such as medical diagnostics reports. While extracting knowledge from those sensitive data, both privacy of individuals’ and the utility of data should be preserved which is a crucial concern in data mining related activities. Though therein exist several methods to preserve privacy, a single method can not maintain the harmony interim privacy and data utility. Often achieving individuals’ privacy leads to the loss of the data utility and the opposite is true also. To address the vital issue, a four-stage data perturbation approach, called NRoReM, is proposed in this work based on normalization, geometric rotation, linear regression, and scalar multiplication for sensitive data mining. The proposed approach is experimented with over ten UCI data set using three benchmark classifiers. The empirical exploration of privacy protection, attack resistance, information entropy analysis, data utility, and error analysis exhibits that NRoReM preserves both privacy of individuals’ and data utility on a larger scale for 90% of the data set than 3-Dimensional Rotation Transformation (3DRT) and 2-Dimensional Rotation Transformation (2DRT).

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Cited By

Paul MPal BSattar ASiddique AHasan M(2024)CARBOKnowledge-Based Systems10.1016/j.knosys.2024.112196300:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112196
Hewage USinha RNaeem M(2023)Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature reviewArtificial Intelligence Review10.1007/s10462-023-10425-356:9(10427-10464)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1007/s10462-023-10425-3

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An efficient perturbation approach for multivariate data in sensitive and reliable data mining

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cover image Journal of Information Security and Applications

Journal of Information Security and Applications Volume 62, Issue C

Nov 2021

144 pages

ISSN:2214-2126

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Elsevier Ltd.

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Elsevier Science Inc.

United States

Publication History

Published: 01 November 2021

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Cited By

Paul MPal BSattar ASiddique AHasan M(2024)CARBOKnowledge-Based Systems10.1016/j.knosys.2024.112196300:COnline publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112196
Hewage USinha RNaeem M(2023)Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature reviewArtificial Intelligence Review10.1007/s10462-023-10425-356:9(10427-10464)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1007/s10462-023-10425-3

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