Towards Providing Automated Feedback on the Quality of Inferences from Synthetic Datasets
When releasing individual-level data to the public, statistical agencies typically alter data values to protect the confidentiality of individuals’ identities and sensitive attributes. When data undergo substantial perturbation, secondary data analysts’ inferences can be distorted in ways that they...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Labor Dynamics Institute
2012-07-01
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| Series: | The Journal of Privacy and Confidentiality |
| Subjects: | |
| Online Access: | https://journalprivacyconfidentiality.org/index.php/jpc/article/view/616 |
| _version_ | 1818566491100938240 |
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| author | David R. McClure Jerome P. Reiter |
| author_facet | David R. McClure Jerome P. Reiter |
| author_sort | David R. McClure |
| collection | DOAJ |
| description | When releasing individual-level data to the public, statistical agencies typically alter data values to protect the confidentiality of individuals’ identities and sensitive attributes. When data undergo substantial perturbation, secondary data analysts’ inferences can be distorted in ways that they typically cannot determine from the released data alone. This is problematic, in that analysts have no idea if they should trust the results based on the altered data.To ameliorate this problem, agencies can establish verification servers, which are remote computers that analysts query for measures of the quality of inferences obtained from disclosure-protected data. The reported quality measures reflect the similarity between the analysis done with the altered data and the analysis done with the confidential data. However, quality measures can leak information about the confidential values, so that they too must be subject to disclosure protections. In this article, we discuss several approaches to releasing quality measures for verification servers when the public use data are generated via multiple imputation, also known as synthetic data. The methods can be modified for other stochastic perturbation methods. |
| first_indexed | 2024-12-14T01:54:21Z |
| format | Article |
| id | doaj.art-cc0c22ae342c4364844fe42c367b670e |
| institution | Directory Open Access Journal |
| issn | 2575-8527 |
| language | English |
| last_indexed | 2024-12-14T01:54:21Z |
| publishDate | 2012-07-01 |
| publisher | Labor Dynamics Institute |
| record_format | Article |
| series | The Journal of Privacy and Confidentiality |
| spelling | doaj.art-cc0c22ae342c4364844fe42c367b670e2022-12-21T23:21:16ZengLabor Dynamics InstituteThe Journal of Privacy and Confidentiality2575-85272012-07-014110.29012/jpc.v4i1.616Towards Providing Automated Feedback on the Quality of Inferences from Synthetic DatasetsDavid R. McClure0Jerome P. Reiter1Department of Statistical Science, Duke University, Durham, NCDepartment of Statistical Science, Duke University, Durham, NCWhen releasing individual-level data to the public, statistical agencies typically alter data values to protect the confidentiality of individuals’ identities and sensitive attributes. When data undergo substantial perturbation, secondary data analysts’ inferences can be distorted in ways that they typically cannot determine from the released data alone. This is problematic, in that analysts have no idea if they should trust the results based on the altered data.To ameliorate this problem, agencies can establish verification servers, which are remote computers that analysts query for measures of the quality of inferences obtained from disclosure-protected data. The reported quality measures reflect the similarity between the analysis done with the altered data and the analysis done with the confidential data. However, quality measures can leak information about the confidential values, so that they too must be subject to disclosure protections. In this article, we discuss several approaches to releasing quality measures for verification servers when the public use data are generated via multiple imputation, also known as synthetic data. The methods can be modified for other stochastic perturbation methods.https://journalprivacyconfidentiality.org/index.php/jpc/article/view/616ConfidentialityDisclosureMultiple imputationUtilityVerification |
| spellingShingle | David R. McClure Jerome P. Reiter Towards Providing Automated Feedback on the Quality of Inferences from Synthetic Datasets The Journal of Privacy and Confidentiality Confidentiality Disclosure Multiple imputation Utility Verification |
| title | Towards Providing Automated Feedback on the Quality of Inferences from Synthetic Datasets |
| title_full | Towards Providing Automated Feedback on the Quality of Inferences from Synthetic Datasets |
| title_fullStr | Towards Providing Automated Feedback on the Quality of Inferences from Synthetic Datasets |
| title_full_unstemmed | Towards Providing Automated Feedback on the Quality of Inferences from Synthetic Datasets |
| title_short | Towards Providing Automated Feedback on the Quality of Inferences from Synthetic Datasets |
| title_sort | towards providing automated feedback on the quality of inferences from synthetic datasets |
| topic | Confidentiality Disclosure Multiple imputation Utility Verification |
| url | https://journalprivacyconfidentiality.org/index.php/jpc/article/view/616 |
| work_keys_str_mv | AT davidrmcclure towardsprovidingautomatedfeedbackonthequalityofinferencesfromsyntheticdatasets AT jeromepreiter towardsprovidingautomatedfeedbackonthequalityofinferencesfromsyntheticdatasets |