## What is/are Randomized Response?

Randomized Response - Bouza (2009) investigated the benefits of using ranked set sampling in the randomized response setup introduced by Chaudhury and Stenger (1992).^{[1]}Especially our two proposed methods are the Laplace Mechanism-based Database Watermarking (LMDW) and Randomized Response-based Database Watermarking (RRDW) for two classical local differential privacy mechanisms the Laplace Mechanism (LM) and the Randomized Response (RR) respectively.

^{[2]}A recommendation algorithm based on collaborative filtering, matrix factorization as well as the randomized response is proposed, which satisfies local differential privacy (LDP).

^{[3]}While hard-garbling does improve information transmission over direct-elicitation, other predictions fail: randomized response performs much better than expected; and false accusations lead to a small but persistent bias in treatment effect estimates.

^{[4]}Background: Nonrandomized response (NRR) models are a new generation of surveys for sensitive issues.

^{[5]}We used the randomized response technique (RRT) to estimate the prevalence and drivers of illegal hunting targeting four focal bird taxa (barbets, bulbuls, partridges, and pheasants).

^{[6]}In this work, we employ advancements in randomized response techniques to overcome the neglect of respondents to truthfully reveal deceitful behaviour.

^{[7]}Warner’s randomized response (RR) model is used to collect sensitive information for a broad range of surveys, but it possesses several limitations such as lack of reproducibility, higher costs and it is not feasible for mail questionnaires.

^{[8]}The probably most traditional method is the Randomized Response Technique by Warner (1965).

^{[9]}The randomized responses procedure due to Warner (1965) is used for eliminating answer biases.

^{[10]}We implement our procedure and use it for (dis)proving privacy bounds for many well-known examples, including randomized response, histogram, report noisy max and sparse vector.

^{[11]}To mitigate the response distortion arising from dishonest answers to sensitive questions, the randomized response technique (RRT) is a useful and effective statistical method.

^{[12]}Much empirical evidence has shown that the randomized response technique is useful for the collection of truthful responses.

^{[13]}ABSTRACT This study focuses on the estimation of population mean of a sensitive variable in stratified random sampling based on randomized response technique (RRT) when the observations are contaminated by measurement errors (ME).

^{[14]}The key idea behind our approach is to marry two techniques together, namely, sampling (used for approximate computation) and randomized response (used for privacypreserving analytics).

^{[15]}This paper investigated the rule breaking conduct in a Nigerian protected forest reserve area in order to exploit natural resources using Randomized Response Technique (RRT) for data collection.

^{[16]}Randomized response model is one of the most recent methods which is attracting the attention of survey practitioners to deal with the problems of non-response because it protects the privacy of individuals in order to acquire the truthful response.

^{[17]}In our mechanism, users perturb their ratings locally on their devices using Laplace and randomized response mechanisms and send the perturbed ratings to the service provider.

^{[18]}We measured the prevalence of competition manipulation by German elite athletes and the total percentage of these athletes who had been asked to participate in match fixing by using the randomized response technique.

^{[19]}In this research, we address this challenge using a combination of a randomized response (RR) approach for data collection and a multiscale item response theory (IRT) model for data analysis.

^{[20]}The operating characteristics (OCs) of a subset ranking and selection procedure are derived for a randomized response model for continuous data.

^{[21]}For the discussion of these different aspects of privacy protection, a family of randomized response techniques enabling the tailoring of the design’s privacy protection to the respondents is presented as representative of indirect questioning designs.

^{[22]}The paper formalizes Warner's (1965) randomized response technique (RRT) as a game and implements it experimentally, thus linking game theoretic approaches to randomness in communication with survey practice in the field and a novel implementation in the lab.

^{[23]}We consider a problem of analyzing a global property of private data through randomized responses subject to a certain rule, where private data are used for another cryptographic protocol, e.

^{[24]}The seminal work of Warner (1965) on randomized response has motivated the development of a fruitful theory.

^{[25]}2013; Lyall, Blair, and Imai 2013), randomized response technique (Blair, Imai, and Zhou 2015), or the list experiment.

^{[26]}Indirect question formats, such as the Item Count Technique (ICT) and the Randomized Response Techniques (RRT), including the Crosswise Model (CM) and the Triangular Model (TM), have been developed to protect respondents’ privacy by design to elicit more truthful answers.

^{[27]}Generalization and randomized response methods were proposed in database community to tackle this problem.

^{[28]}We suggest for further study an idea to construct strata boundaries using ranked set sampling for randomized response technique, introduced by Bouza (2009).

^{[29]}Various indirect questioning methods have been developed to reduce SDB and increase data reliability, one of them being the randomized response technique (RRT).

^{[30]}

## unequal probability sampling

There are nine well-written chapters covering a range of topics includingmotivation to sampling, concepts of population versus sample, random sampling with and without replacement, estimation, sample size determination, unequal probability sampling, stratified sampling, cluster sampling, multi-stage sampling, regression estimation, super population modeling, Bayesian methods, spatial smoothing, successive sampling, handling non-responses, imputations, repeated sampling, randomized responses to obtain better responses, indirect questioning, small domain statistics, network sampling, adaptive sampling, and Jack-knifing among others.^{[1]}ABSTRACT In this paper, Abdelfatah and Mazloum's (2015) two-stage randomized response model is extended to unequal probability sampling and stratified unequal probability sampling, both with and without replacement.

^{[2]}

## Optional Randomized Response

Thus, the optional randomized response model , where k is a random variable having value 1 if the response is scrambled and 0 otherwise, was considered for finding out Approximate Optimum Strata Boundaries by minimizing the variance of the estimator.^{[1]}and Huang considered optional randomized response techniques where the probability of choosing the randomized (or direct) response is fixed for all the respondents.

^{[2]}In this study, we propose optional randomized response technique (RRT) models in binary response situation.

^{[3]}Gupta et al (2002) suggested an optional randomized response model under the assumption that the mean of the scrambling variable S is ‘unity’ [i.

^{[4]}This is done by using optional randomized response.

^{[5]}

## Stage Randomized Response

Our solution relies on a distributed client-server architecture and a two-stage Randomized Response algorithm, along with an implementation on the popular recommendation model, Matrix Factorization (MF).^{[1]}ABSTRACT In this paper, Abdelfatah and Mazloum's (2015) two-stage randomized response model is extended to unequal probability sampling and stratified unequal probability sampling, both with and without replacement.

^{[2]}

## Scrambled Randomized Response

In this study, we consider variance estimation procedure using scrambled randomized response for sensitive variable using multi-auxiliary variables in multi-phase sampling.^{[1]}With the intention to control a true swapping between the efficiency and the privacy protection this paper introduces a scrambled randomized response (SRR) model to be alternative of Saha’s scrambling mechanism.

^{[2]}

## Question Randomized Response

In this paper, we developed a new unique unrelated question randomized response model in which each card has two questions, either both questions on the sensitive characteristics or both questions on the two unrelated characteristics.^{[1]}A shrinkage estimator of population mean using a prior information is proposed under unrelated question randomized response model where one of the two questions presented to the respondents is non-stigmatized and unrelated to the stigmatized character.

^{[2]}

## Symmetric Randomized Response

We show that the Bayesian-Nash equilibrium can be in the form of either a symmetric randomized response (SR) strategy or an informative non-disclosive (ND) strategy.^{[1]}Our findings reveal that, the Bayesian-Nash equilibrium can be in the form of either a symmetric randomized response (SR) strategy or an informative non-disclosive (ND) strategy.

^{[2]}

## Alternative Randomized Response

In this article, alternative randomized response models are proposed, which make use of sum of quantitative scores generated from two decks of cards being used in a survey.^{[1]}This paper proposes an alternative randomized response technique by improving existing works on tripartite randomized response technique (TRRT) using unrelated questions.

^{[2]}

## New Randomized Response

This paper suggests a new randomized response model useful for gathering information on quantitative sensitive variable such as drug usage, tax evasion and induced abortions etc.^{[1]}In this article, we propose a new randomized response model to estimate the population total of a sensitive variable of quantitative nature.

^{[2]}

## randomized response technique

The use of scramble variable is considered herein randomized response technique to estimate the parameters of the sensitive variable.^{[1]}In this study, we introduce a mixture binary Randomized Response Technique (RRT) model by combining the elements of the Greenberg Unrelated Question model and the Warner Indirect Question model.

^{[2]}Nonetheless, they have a very sparse presence in finite population sampling when sensitive topics are investigated and data are obtained by means of the randomized response technique (RRT), a survey method based on the principle that sensitive questions must not be asked directly to the respondents.

^{[3]}For more reliable information, the randomized response technique is often used.

^{[4]}We propose simple internal consistency tests for two such methods, the list experiment and the randomized response technique (its Warner and Crosswise variants).

^{[5]}We compared how indirect (randomized response technique) and direct questioning techniques performed when assessing non-sensitive (fish consumption, used as negative control) and sensitive (illegal consumption of wild animals) behaviors across an urban gradient (small towns, large towns, and the large city of Manaus) in the Brazilian Amazon.

^{[6]}The randomized response technique (RRT) is an effective method designed to obtain the stigmatized information from respondents while assuring the privacy.

^{[7]}Unrelated characteristics model (URL) is a type of randomized response technique (RRT) used to estimate finite population proportion of individuals bearing such a sensitive characteristic whose com.

^{[8]}To obtain trustworthy data and to reduce false response bias, a technique, known as randomized response technique, is now being used in many surveys.

^{[9]}The present study proposes a generalized mean estimator for a sensitive variable using a non-sensitive auxiliary variable in the presence of measurement errors based on the Randomized Response Technique (RRT).

^{[10]}Tripartite Randomized Response Technique (TRRT) and the Direct Method (DM) were applied in the estimation of proportion.

^{[11]}and Huang considered optional randomized response techniques where the probability of choosing the randomized (or direct) response is fixed for all the respondents.

^{[12]}We used the randomized response technique (RRT) to estimate the prevalence and drivers of illegal hunting targeting four focal bird taxa (barbets, bulbuls, partridges, and pheasants).

^{[13]}In this work, we employ advancements in randomized response techniques to overcome the neglect of respondents to truthfully reveal deceitful behaviour.

^{[14]}The probably most traditional method is the Randomized Response Technique by Warner (1965).

^{[15]}In this study, we propose optional randomized response technique (RRT) models in binary response situation.

^{[16]}To mitigate the response distortion arising from dishonest answers to sensitive questions, the randomized response technique (RRT) is a useful and effective statistical method.

^{[17]}Much empirical evidence has shown that the randomized response technique is useful for the collection of truthful responses.

^{[18]}ABSTRACT This study focuses on the estimation of population mean of a sensitive variable in stratified random sampling based on randomized response technique (RRT) when the observations are contaminated by measurement errors (ME).

^{[19]}This paper investigated the rule breaking conduct in a Nigerian protected forest reserve area in order to exploit natural resources using Randomized Response Technique (RRT) for data collection.

^{[20]}In this paper, we improve the efficiency of Koyuncu et al (2014)’s estimator of population mean of sensitive variable by replacing Traditional Randomized response technique with Optional Randomized response technique as suggested by Gupta et al (2014).

^{[21]}This paper proposes an alternative randomized response technique by improving existing works on tripartite randomized response technique (TRRT) using unrelated questions.

^{[22]}We measured the prevalence of competition manipulation by German elite athletes and the total percentage of these athletes who had been asked to participate in match fixing by using the randomized response technique.

^{[23]}For the discussion of these different aspects of privacy protection, a family of randomized response techniques enabling the tailoring of the design’s privacy protection to the respondents is presented as representative of indirect questioning designs.

^{[24]}The paper formalizes Warner's (1965) randomized response technique (RRT) as a game and implements it experimentally, thus linking game theoretic approaches to randomness in communication with survey practice in the field and a novel implementation in the lab.

^{[25]}2013; Lyall, Blair, and Imai 2013), randomized response technique (Blair, Imai, and Zhou 2015), or the list experiment.

^{[26]}In this article, we propose a new partial randomized response technique (RRT) model to estimate the mean of the number of persons possessing a rare sensitive attribute using the Poisson distribution.

^{[27]}Indirect question formats, such as the Item Count Technique (ICT) and the Randomized Response Techniques (RRT), including the Crosswise Model (CM) and the Triangular Model (TM), have been developed to protect respondents’ privacy by design to elicit more truthful answers.

^{[28]}To resolve the privacy issues in such scenarios, the DPWeVote protocol is proposed which incorporates Randomized Response technique and consists the following three phases: the Randomized Weights Collection phase, the Randomized Opinions Collection phase, and the Voting Results Release phase.

^{[29]}We suggest for further study an idea to construct strata boundaries using ranked set sampling for randomized response technique, introduced by Bouza (2009).

^{[30]}Various indirect questioning methods have been developed to reduce SDB and increase data reliability, one of them being the randomized response technique (RRT).

^{[31]}

## randomized response model

In carrying out surveys involving sensitive characteristics, randomized response models have been considered among the best techniques since they provide the maximum privacy protection to the respo.^{[1]}However, a sample size determination method for complex sampling surveys of sensitive issues using a randomized response model is not yet available.

^{[2]}In this paper, we developed a new unique unrelated question randomized response model in which each card has two questions, either both questions on the sensitive characteristics or both questions on the two unrelated characteristics.

^{[3]}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.

^{[4]}Thus, the optional randomized response model , where k is a random variable having value 1 if the response is scrambled and 0 otherwise, was considered for finding out Approximate Optimum Strata Boundaries by minimizing the variance of the estimator.

^{[5]}This paper suggests a new randomized response model useful for gathering information on quantitative sensitive variable such as drug usage, tax evasion and induced abortions etc.

^{[6]}In this article, we propose a new randomized response model to estimate the population total of a sensitive variable of quantitative nature.

^{[7]}ABSTRACT This article suggests an efficient method of estimating a rare sensitive attribute which is assumed following Poisson distribution by using three-stage unrelated randomized response model instead of the Land et al.

^{[8]}A shrinkage estimator of population mean using a prior information is proposed under unrelated question randomized response model where one of the two questions presented to the respondents is non-stigmatized and unrelated to the stigmatized character.

^{[9]}ABSTRACT In this paper, Abdelfatah and Mazloum's (2015) two-stage randomized response model is extended to unequal probability sampling and stratified unequal probability sampling, both with and without replacement.

^{[10]}Gupta et al (2002) suggested an optional randomized response model under the assumption that the mean of the scrambling variable S is ‘unity’ [i.

^{[11]}In this article, alternative randomized response models are proposed, which make use of sum of quantitative scores generated from two decks of cards being used in a survey.

^{[12]}In this paper, a new additive randomized response model has been proposed.

^{[13]}Randomized response model is one of the most recent methods which is attracting the attention of survey practitioners to deal with the problems of non-response because it protects the privacy of individuals in order to acquire the truthful response.

^{[14]}The operating characteristics (OCs) of a subset ranking and selection procedure are derived for a randomized response model for continuous data.

^{[15]}ABSTRACT This paper proposes an efficient stratified randomized response model based on Chang et al.

^{[16]}

## randomized response algorithm

To measure the privacy guarantee of an algorithm, we use the concept of differential privacy and use the randomized response algorithm to generate differentially private data.^{[1]}Our solution relies on a distributed client-server architecture and a two-stage Randomized Response algorithm, along with an implementation on the popular recommendation model, Matrix Factorization (MF).

^{[2]}