Disclosure Risk Evaluation for Fully Synthetic Categorical Data

Hu, Jingchen; Reiter, Jerome P.; Wang, Quanli

doi:10.1007/978-3-319-11257-2_15

Cited by 47 publications

(79 citation statements)

References 26 publications

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“…This has been confirmed by studies evaluating their risks [11,[16][17][18][19]. Elliot [20] came to a similar conclusion in his report on the disclosure risk associated with the synthetic data produced using the synthpop package.…”

Section: Implementation Of Synthetic Data In the Scottishmentioning

confidence: 61%

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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Section: Implementation Of Synthetic Data In the Scottishmentioning

confidence: 61%

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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show abstract

“…Afterward, many techniques and tools have been proposed for generating synthetic datasets and populations [7,8,23]. Similarly, other research efforts have focused on evaluating the privacy of synthetic methods and avoid the risk of disclosure [18,22]. In our work, we focus on generating synthetic microdata, not as a technique of data protection, but as a mechanism to improve the testing of anonymization techniques.…”

Section: Introductionmentioning

confidence: 99%

COCOA: A Synthetic Data Generator for Testing Anonymization Techniques

Ayala-Rivera

Portillo-Dominguez

Murphy

et al. 2016

Privacy in Statistical Databases

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Abstract. Conducting extensive testing of anonymization techniques is critical to assess their robustness and identify the scenarios where they are most suitable. However, the access to real microdata is highly restricted and the one that is publicly-available is usually anonymized or aggregated; hence, reducing its value for testing purposes. In this paper, we present a framework (COCOA) for the generation of realistic synthetic microdata that allows to define multi-attribute relationships in order to preserve the functional dependencies of the data. We prove how COCOA is useful to strengthen the testing of anonymization techniques by broadening the number and diversity of the test scenarios. Results also show how COCOA is practical to generate large datasets.

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“…We describe this in Section 2.3 and use it in the empirical part. An alternative solution is in the use of synthetic data and a related notion of differential privacy, for example, Dwork and Nissim (2004), Dwork (2006), and Abowd and Vilhuber (2008), as well as Duncan and Lambert (1986), Duncan and Mukherjee (1991), Duncan and Pearson (1991), Fienberg (1994Fienberg ( , 2001, and Duncan, Fienberg, Krishnan, Padman, and Roehrig (2001), Abowd and Woodcock (2001), Kinney, Reiter, Reznek, Miranda, Jarmin, andAbowd (2011), andHu, Reiter, andWang (2014), among others.…”

Section: Related Literaturementioning

confidence: 99%

Identification, Data Combination and the Risk of Disclosure

2014

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It is commonplace that the data needed for econometric inference are not contained in a single source. In this paper we analyze the problem of parametric inference from combined individual-level data when data combination is based on personal and demographic identifiers such as name, age, or address. Our main question is the identification of the econometric model based on the combined data when the data do not contain exact individual identifiers and no parametric assumptions are imposed on the joint distribution of information that is common across the combined data set. We demonstrate the conditions on the observable marginal distributions of data in individual data sets that can and cannot guarantee identification of the parameters of interest. We also note that the data combination procedure is essential in a semiparametric setting such as ours. Provided that the (nonparametric) data combination procedure can only be defined in finite samples, we introduce a new notion of identification based on the concept of limits of statistical experiments. Our results apply to the setting where the individual data used for inferences are sensitive and their combination may lead to a substantial increase in the data sensitivity or lead to a "de-anonymization" of the previously "anonymized" information. We demonstrate that the point identification of an econometric model from combined data is incompatible with restrictions on the risk of individual disclosure. If the data combination procedure guarantees a bound on the risk of individual disclosure, then the information available from the combined data set allows one to identify the parameter of interest only partially, and the size of the identification region is inversely related to the upper bound guarantee for the disclosure risk. This result is new in the context of data combination as we notice that the quality of links that need to be used in the combined data to assure point identification may be much higher than the average link quality in the entire data set, and thus point inference requires the use of the 396 Komarova, Nekipelov, and Yakovlev Quantitative Economics 9 (2018) most sensitive subset of the data. Our results provide important insights into the ongoing discourse on the empirical analysis of merged administrative records as well as discussions on the "disclosive" nature of policies implemented by the datadriven companies (such as internet services companies and medical companies using individual patient records for policy decisions).

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Disclosure Risk Evaluation for Fully Synthetic Categorical Data

Cited by 47 publications

References 26 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

COCOA: A Synthetic Data Generator for Testing Anonymization Techniques

Identification, Data Combination and the Risk of Disclosure

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