Synthetic Datasets for Statistical Disclosure Control

Drechsler, Jörg

doi:10.1007/978-1-4614-0326-5

Cited by 136 publications

(87 citation statements)

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“…Consider as an example a default synthesis, i. 3 and so on. The distribution of the last variable Y p will be conditional on all other variables.…”

Section: Methodsmentioning

confidence: 99%

“…At the same time conclusions that can be drawn from such data should not be compromised. Despite the attractiveness of this approach and recent developments in this area [2][3][4][5][6][7][8][9], the practical examples of synthetic data products are very limited [4,10,11] and their users are urged to request validation of results with original data. This is not surprising, because preserving all possible relationships between variables in a data set can be next to impossible.…”

Section: Introductionmentioning

confidence: 99%

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Providing bespoke synthetic data for the UK Longitudinal Studies and other sensitive data with the synthpop package for R1

Nowok¹,

Raab²,

Dibben³

2017

SJI

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Abstract. Synthetic data methods were designed to address the conflicting demands placed on data holders to unlock the research and policy potential of microdata while at the same time preserving the confidentiality of individuals. Recently, these methods have become more widely recognized in the UK and the provision of bespoke synthetic data has been approved to expand the use of one of the UK Longitudinal Studies. The process of producing useful synthetic data involves, however, a substantial investment of research time, as it always requires some customising for the characteristics of an individual data set. At the same time, a substantial part of it can be automated and this is essential when the process has to be conducted rapidly and on a regular basis. This paper describes the application of synthetic data to the UK Longitudinal Studies, details implementation process for the Scottish Longitudinal Study and presents methods used in an R package synthpop that has been developed to facilitate production of non-disclosive entirely synthetic data. A reproducible example using open data is given to illustrate the synthesising procedure and to provide insights into quality of synthetic data generated using different automated approaches.

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“…Consider as an example a default synthesis, i. 3 and so on. The distribution of the last variable Y p will be conditional on all other variables.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Providing bespoke synthetic data for the UK Longitudinal Studies and other sensitive data with the synthpop package for R1

Nowok¹,

Raab²,

Dibben³

2017

SJI

View full text Add to dashboard Cite

show abstract

“…This approach was suggested by Little (1993), and methods for drawing inference based on synthetic data under this approach were developed by Reiter (2003). We refer to the monograph by Drechsler (2011) for a detailed and general discussion on synthetic data methodology.…”

Section: Introductionmentioning

confidence: 99%

Likelihood Based Finite Sample Inference for Singly Imputed Synthetic Data Under the Multivariate Normal and Multiple Linear Regression Models

Kampel

Sinha

2015

JPC

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In this paper we develop likelihood-based finite sample inference based on singly imputed partially synthetic data, when the original data follow either a multivariate normal or a multiple linear regression model. We assume that the synthetic data are generated by using the plug-in sampling method, where unknown parameters in the data model are set equal to observed values of their point estimators based on the original data, and synthetic data are drawn from this estimated version of the model. Empirical studies are presented to show that the proposed methods do indeed perform as the theory predicts, and to compare the proposed methods for singly imputed synthetic data with the combining rules that are used to analyze multiply imputed partially synthetic data. Some theoretical comparisons between singly and multiply imputed partially synthetic data inference are also provided. A data analysis example and disclosure risk evaluation of singly and multiply imputed partially synthetic data is presented based on public use data from the Current Population Survey. We discuss the specific conditions under which the proposed methodology will yield valid inference, and evaluate the performance of the methodology when certain conditions do not hold. We outline some ways to extend the proposed methodology for certain scenarios where the required set of conditions do not hold.

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“…Rubin [36] proposed using MI as a method of sensitive data protection. The rigorous foundations for MI as a method for sensitive data protection were further developed (e.g., [10]; [9]; [26]; [27,28,29,30]) and this still continues to be an active area of research. We refer to the recent monograph by Drechsler [10] for a detailed and general discussion of multiple imputation as a tool for disclosure control.…”

Section: Introductionmentioning

confidence: 99%

“…The rigorous foundations for MI as a method for sensitive data protection were further developed (e.g., [10]; [9]; [26]; [27,28,29,30]) and this still continues to be an active area of research. We refer to the recent monograph by Drechsler [10] for a detailed and general discussion of multiple imputation as a tool for disclosure control. An and Little [5] and Jenkins et al [15] argue that synthetic data methodology provides an attractive alternative to top coding, and a remedy to some of the drawbacks of top coding, because analysis of the synthetic data is straightforward for a data user, and some information contained in the top part of the data is retained.…”

Section: Introductionmentioning

confidence: 99%

Noise Multiplication for Statistical Disclosure Control of Extreme Values in Log-normal Regression Samples

Kampel

Mathew

Sinha

2014

JPC

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In this article multiplication of original data values by random noise is suggested as a disclosure control strategy when only the top part of the data is sensitive, as is often the case with income data. The proposed method can serve as an alternative to top coding which is a standard method in this context. Because the log-normal distribution usually fits income data well, the present investigation focuses exclusively on the log-normal. It is assumed that the log-scale mean of the sensitive variable is described by a linear regression on a set of non-sensitive covariates, and we show how a data user can draw valid inference on the parameters of the regression. An appealing feature of noise multiplication is the presence of an explicit tuning mechanism, namely, the noise generating distribution. By appropriately choosing this distribution, one can control the accuracy of inferences and the level of disclosure protection desired in the released data. Usually, more information is retained on the top part of the data under noise multiplication than under top coding. Likelihood based analysis is developed when only the large values in the data set are noise multiplied, under the assumption that the original data form a sample from a log-normal distribution. In this scenario, data analysis methods are developed under two types of data releases: (I) each released value includes an indicator of whether or not it has been noise multiplied, and (II) no such indicator is provided. A simulation study is carried out to assess the accuracy of inference for some parameters of interest. Since top coding and synthetic data methods are already available as disclosure control strategies for extreme values, some comparisons with the proposed method are made through a simulation study. The results are illustrated with a data analysis example based on 2000 U.S. Current Population Survey data. Furthermore, a disclosure risk evaluation of the proposed methodology is presented in the context of the Current Population Survey data example, and the disclosure risk of the proposed noise multiplication method is compared with the disclosure risk of synthetic data.

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Synthetic Datasets for Statistical Disclosure Control

Cited by 136 publications

References 0 publications

Providing bespoke synthetic data for the UK Longitudinal Studies and other sensitive data with the synthpop package for R1

Providing bespoke synthetic data for the UK Longitudinal Studies and other sensitive data with the synthpop package for R1

Likelihood Based Finite Sample Inference for Singly Imputed Synthetic Data Under the Multivariate Normal and Multiple Linear Regression Models

Noise Multiplication for Statistical Disclosure Control of Extreme Values in Log-normal Regression Samples

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