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 |
| Published: |
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 |