Stable Feature Selection with Privacy Preserving Data Mining Algorithm

In this study, a new model is provided for customized privacy in privacy preserving data mining in which the data owners define different levels for privacy for different features. Additionally, in order to improve perturbation methods, a method combined of singular value decomposition (SVD) and feature selection methods is defined so as to benefit from the advantages of both domains. Also, to assess the amount of distortion created by the proposed perturbation method, new distortion criteria are defined in which the amount of created distortion in the process of feature selection is considered based on the value of privacy in each feature. Different tests and results analysis show that offered method based on this model compared to previous approaches, caused the improved privacy, accuracy of mining results and efficiency of privacy preserving data mining systems.

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A Perturbation Method Based on Singular Value Decomposition and Feature Selection for Privacy Preserving Data Mining

Business Intelligence ◽

10.4018/978-1-4666-9562-7.ch015 ◽

2016 ◽

pp. 281-304

Author(s):

Mohammad Reza Keyvanpour ◽

Somayyeh Seifi Moradi

Keyword(s):

Data Mining ◽

Feature Selection ◽

Singular Value Decomposition ◽

Perturbation Method ◽

Privacy Preserving ◽

Singular Value ◽

Privacy Preserving Data Mining ◽

New Model ◽

Value Decomposition ◽

Different Levels

In this study, a new model is provided for customized privacy in privacy preserving data mining in which the data owners define different levels for privacy for different features. Additionally, in order to improve perturbation methods, a method combined of singular value decomposition (SVD) and feature selection methods is defined so as to benefit from the advantages of both domains. Also, to assess the amount of distortion created by the proposed perturbation method, new distortion criteria are defined in which the amount of created distortion in the process of feature selection is considered based on the value of privacy in each feature. Different tests and results analysis show that offered method based on this model compared to previous approaches, caused the improved privacy, accuracy of mining results and efficiency of privacy preserving data mining systems.

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Secure Computation for Privacy Preserving Data Mining

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch266 ◽

2011 ◽

pp. 1747-1752 ◽

Cited By ~ 1

Author(s):

Yehuda Lindell

Keyword(s):

Data Mining ◽

Privacy Preserving ◽

Secure Computation ◽

Data Mining Algorithm ◽

Privacy Preserving Data Mining ◽

Public And Private ◽

The Public ◽

Use Of Data ◽

To Come ◽

Mining Tools

The increasing use of data mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy preserving data mining solutions achieve the somewhat paradoxical property of enabling a data mining algorithm to use data without ever actually “seeing” it. Thus, the benefits of data mining can be enjoyed, without compromising the privacy of concerned individuals.

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