Utility and Risk Evaluation of Synthetic Data by Orthogonal Transformation

Natsuki Sano ()
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Natsuki Sano: Tokyo University of Information Sciences

The Review of Socionetwork Strategies, 2022, vol. 16, issue 1, 71-79

Abstract: Abstract Releasing synthetic data in statistical disclosure control makes identifying individual records difficult, as synthetic data differ from the original data. We propose a method for generating synthetic data using orthogonal transformation, along with a utility measure for the data thus generated. This method can control the utility of the generated synthetic data in terms of the proposed utility measure. We applied the method to anonymized data, obtained from a national survey of family income and expenditure in Japan, and generated synthetic data for it. Additionally, we evaluated the utility and risk of the data thus generated and compared them with synthetic data generated through other methods. We find that the proposed method can generate synthetic data with a higher utility than other methods by adjustment of the number of adopted eigen value.

Keywords: Statistical disclosure control; Synthetic data; Data risk; Data utility; Principal component analysis; Imputation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12626-022-00107-x

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