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INVESTIGATING ATTRIBUTE RISKS AND CONSTRUCTING LINKAGE ERROR MODELS FOR PROBABILISTICALLY-LINKED DATA

Part of: Stochastic analysis

Published online by Cambridge University Press:  19 April 2021

Y. MA Open the ORCID record for Y. MA [Opens in a new window]
Y. MA*
Affiliation:
School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, New South Wales 2522, Australia
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Abstract

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Keywords

data privacyrecord linkageofficial statisticsnoise multiplication maskingremote systemlinkage error modelmicrodatatabular datastatistical disclosure limitation

MSC classification

Secondary: 60H40: White noise theory
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 104 , Issue 2 , October 2021 , pp. 346 - 348
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Copyright
© 2021 Australian Mathematical Publishing Association Inc.

Footnotes

Thesis submitted to the University of Wollongong in March 2019; degree approved on 18 September 2020; supervisors Yan-Xia Lin, James Chipperfield and Pavel Krivitsky.

References

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Lin, Y. X. and Wise, P., ‘Estimation of regression parameters from noise multiplied data’, J. Priv. Confid. 4 (2012), 61–94.Google Scholar
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Ma, Y., Lin, Y. X. and Sarathy, R., ‘The vulnerability of multiplicative noise protection to correlation- attacks on continuous microdata’, Sankhyā Ser. B 82 (2020), 305–327.CrossRefGoogle Scholar
Thompson, G., Broadfoot, S. and Elazar, D., ‘Methodology for the automatic confidentialisation of statistical outputs from remote servers at the Australian Bureau of Statistics’, in: Joint UNECE/Eurostat Work Session on Statistical Data Confidentiality (UNECE, Ottawa, 2013).Google Scholar