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Synthetic Data
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DOI：10.1146/annurev-statistics-040720-031848.png)
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Abstract
En 中文
Demand for access to data, especially data collected using public funds, is ever growing. At the same time, concerns about the disclosure of the identities of and sensitive information about the respondents providing the data are making the data collectors limit the access to data. Synthetic data sets, generated to emulate certain key information found in the actual data and provide the ability to draw valid statistical inferences, are an attractive framework to afford widespread access to data for analysis while mitigating privacy and confidentiality concerns. The goal of this article is to provide a review of various approaches for generating and analyzing synthetic data sets, inferential justification, limitations of the approaches, and directions for future research.
Keywords:
full synthesis
partial synthesis
differential privacy
statistical inference
multiple imputation
Bayesian inference
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