In this paper, we discuss the use of auxiliary information to estimate the population mean of a sensitive variable when data are perturbed by means of three scrambled response devices, namely the additive, the multiplicative and the mixed model. Emphasis is given to the calibration approach, and...

Moving from the scrambling mechanism recently suggested by Saha [25], three scrambled randomized response (SRR) models are introduced with the intent to realize a right trade-off between efficiency and privacy protection. The models perturb the true response on the sensitive variable by...

In this paper, we discuss in a general framework the design-based estimation of population parameters when sensitive data are collected by randomized response techniques. We show in close detail the procedure for estimating the distribution function of a sensitive quantitative variable and how...

In this paper we propose a modified version of the estimator of Hansen and Hurwitz [12] in the case of quantitative sensitive variable and consider a randomization mechanism on the second call that provides privacy protection to the respondents to get truthful information. We use variance of the...

In this paper, we address the problem of estimating the Gini index when data are assumed to be collected through the randomized response method proposed by Greenberg et al. (J Am Stat Assoc 66:243–250 <CitationRef CitationID="CR19">1971</CitationRef>). In the design-based framework, we treat the Gini index as a population functional and...</citationref>