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A Risk-Utility Balancing Approach to Generate Synthetic Microdata

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DLA - Data Linkage and Anonymisation

Generation of synthetic microdata is one of the promising approaches to statistical disclosure control of microdata. Methods for generating synthetic microdata for multivariate continuous variables include noise addition and decomposition of data matrix. We present a framework to generate synthetic data matrix based on resampling from Stiefel manifold and application of Slutsky’s theorem in singular value decomposition. We also derive utility and risk measures based on this theorem and present a risk-utility balancing approach to generate synthetic continuous microdata. We apply our proposed methods to some reference microdata sets and demonstrate the usefulness of our proposed methods.


This talk is part of the Isaac Newton Institute Seminar Series series.

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