Presented by:
Atikur Khan
Date:
Thursday 1st December 2016 - 16:30 to 17:30
Venue:
INI Seminar Room 2
Abstract:
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.
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