A randomized response model for sensitive attribute with privacy measure using Poisson distribution

In sample surveys, when we need information regarding rare sensitive issues which people often do not prefer to share with others. In such situations this is also awkward for interviewers to ask the direct questions related to confidential and private matters of interviewees. An approach towards the...

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Autores principales:	Chandraketu Singh, Garib Nath Singh, Jong-Min Kim
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	62D05 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/11e3590453f0414c85000ca01675368b
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spelling	oai:doaj.org-article:11e3590453f0414c85000ca01675368b2021-11-22T04:21:17ZA randomized response model for sensitive attribute with privacy measure using Poisson distribution2090-447910.1016/j.asej.2021.03.006https://doaj.org/article/11e3590453f0414c85000ca01675368b2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2090447921001568https://doaj.org/toc/2090-4479In sample surveys, when we need information regarding rare sensitive issues which people often do not prefer to share with others. In such situations this is also awkward for interviewers to ask the direct questions related to confidential and private matters of interviewees. An approach towards the open queries about sensitive issues generally results in the high non-response rates or misleading answers. The aim of this paper is to develop an effective randomized response model to overcome with these types of challenges arising due to sensitive nature of characteristic under study. In this paper we have proposed three-stage randomized response model for estimating mean number of individuals who possessed rare sensitive attribute which makes use of Poisson distribution. The properties of the proposed estimation procedures have been deeply examined when the parameter of a rare unrelated attribute is known as well as unknown. Privacy protection of respondents is also an equally important matter of concern. So measure of privacy protection for the proposed randomized response model has also been examined. Empirical studies are performed to support the theoretical results, which show the dominance of the proposed estimators over well-known contemporary estimators. From the findings of this study we may conclude that proposed randomized response model is rewarding in terms of percent relative efficiencies and privacy protection and may be recommended to survey practitioners for real life applications.Chandraketu SinghGarib Nath SinghJong-Min KimElsevierarticle62D05Engineering (General). Civil engineering (General)TA1-2040ENAin Shams Engineering Journal, Vol 12, Iss 4, Pp 4051-4061 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	62D05 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	62D05 Engineering (General). Civil engineering (General) TA1-2040 Chandraketu Singh Garib Nath Singh Jong-Min Kim A randomized response model for sensitive attribute with privacy measure using Poisson distribution
description	In sample surveys, when we need information regarding rare sensitive issues which people often do not prefer to share with others. In such situations this is also awkward for interviewers to ask the direct questions related to confidential and private matters of interviewees. An approach towards the open queries about sensitive issues generally results in the high non-response rates or misleading answers. The aim of this paper is to develop an effective randomized response model to overcome with these types of challenges arising due to sensitive nature of characteristic under study. In this paper we have proposed three-stage randomized response model for estimating mean number of individuals who possessed rare sensitive attribute which makes use of Poisson distribution. The properties of the proposed estimation procedures have been deeply examined when the parameter of a rare unrelated attribute is known as well as unknown. Privacy protection of respondents is also an equally important matter of concern. So measure of privacy protection for the proposed randomized response model has also been examined. Empirical studies are performed to support the theoretical results, which show the dominance of the proposed estimators over well-known contemporary estimators. From the findings of this study we may conclude that proposed randomized response model is rewarding in terms of percent relative efficiencies and privacy protection and may be recommended to survey practitioners for real life applications.
format	article
author	Chandraketu Singh Garib Nath Singh Jong-Min Kim
author_facet	Chandraketu Singh Garib Nath Singh Jong-Min Kim
author_sort	Chandraketu Singh
title	A randomized response model for sensitive attribute with privacy measure using Poisson distribution
title_short	A randomized response model for sensitive attribute with privacy measure using Poisson distribution
title_full	A randomized response model for sensitive attribute with privacy measure using Poisson distribution
title_fullStr	A randomized response model for sensitive attribute with privacy measure using Poisson distribution
title_full_unstemmed	A randomized response model for sensitive attribute with privacy measure using Poisson distribution
title_sort	randomized response model for sensitive attribute with privacy measure using poisson distribution
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/11e3590453f0414c85000ca01675368b
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_version_	1718418206025056256

A randomized response model for sensitive attribute with privacy measure using Poisson distribution

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