当前位置: X-MOL 学术Ain Shams Eng. J. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A randomized response model for sensitive attribute with privacy measure using Poisson distribution
Ain Shams Engineering Journal ( IF 6.0 ) Pub Date : 2021-05-11 , DOI: 10.1016/j.asej.2021.03.006
Chandraketu Singh , Garib Nath Singh , Jong-Min Kim

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.



中文翻译:

基于泊松分布的具有隐私测度的敏感属性随机响应模型

在抽样调查中,当我们需要有关人们通常不愿意与他人分享的罕见敏感问题的信息时。在这种情况下,采访者直接问与受访者的机密和私人事务有关的问题也很尴尬。对敏感问题的公开询问的方法通常会导致高不答复率或误导性答复。本文的目的是开发一种有效的随机响应模型,以克服由于所研究特征的敏感性而引起的这些类型的挑战。在本文中,我们提出了利用泊松分布估计具有稀有敏感属性的个体平均数的三阶段随机响应模型。当罕见的无关属性的参数已知和未知时,已深入研究了所提出的估计程序的特性。受访者的隐私保护也是一个同样重要的问题。因此,还检查了所提出的随机响应模型的隐私保护措施。进行了实证研究以支持理论结果,这表明所提出的估计量优于著名的当代估计量。根据这项研究的结果,我们可以得出结论,建议的随机响应模型在相对效率百分比和隐私保护方面是有益的,并且可以推荐给现实生活应用的调查从业者。受访者的隐私保护也是一个同样重要的问题。因此,还检查了所提出的随机响应模型的隐私保护措施。进行了实证研究以支持理论结果,这表明所提出的估计量优于著名的当代估计量。根据这项研究的结果,我们可以得出结论,建议的随机响应模型在相对效率百分比和隐私保护方面是有益的,并且可以推荐给现实生活应用的调查从业者。受访者的隐私保护也是一个同样重要的问题。因此,还检查了所提出的随机响应模型的隐私保护措施。进行了实证研究以支持理论结果,这表明所提出的估计量优于著名的当代估计量。根据这项研究的结果,我们可以得出结论,建议的随机响应模型在相对效率百分比和隐私保护方面是有益的,并且可以推荐给现实生活应用的调查从业者。这显示了提议的估计量相对于著名的当代估计量的优势。根据这项研究的结果,我们可以得出结论,建议的随机响应模型在相对效率百分比和隐私保护方面是有益的,并且可以推荐给现实生活应用的调查从业者。这显示了提议的估计量相对于著名的当代估计量的优势。根据这项研究的结果,我们可以得出结论,建议的随机响应模型在相对效率百分比和隐私保护方面是有益的,并且可以推荐给现实生活应用的调查从业者。

更新日期:2021-05-11
down
wechat
bug