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Utility and Risk Evaluation of Synthetic Data by Orthogonal Transformation

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  • Natsuki Sano

    (Tokyo University of Information Sciences)

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.

Suggested Citation

  • Natsuki Sano, 2022. "Utility and Risk Evaluation of Synthetic Data by Orthogonal Transformation," The Review of Socionetwork Strategies, Springer, vol. 16(1), pages 71-79, April.
    • Handle: RePEc:spr:trosos:v:16:y:2022:i:1:d:10.1007_s12626-022-00107-x
    DOI: 10.1007/s12626-022-00107-x
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Krishnamurty Muralidhar & Rathindra Sarathy, 2006. "Data Shuffling--A New Masking Approach for Numerical Data," Management Science, INFORMS, vol. 52(5), pages 658-670, May.
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