Perturbed robust linear estimating equations for confidentiality protection in remote analysis

Autor: Atikur R. Khan, Sebastien Lucie, Soonmin Kwon, Christine M. O'Keefe, Tim Ayre, Soomin Song
Rok vydání: 2016
Předmět:
Zdroj: Statistics and Computing. 27:775-787
ISSN: 1573-1375
0960-3174
DOI: 10.1007/s11222-016-9653-2
Popis: National statistical agencies and other data custodians collect and hold a vast amount of survey and census data, containing information vital for research and policy analysis. However, the problem of allowing analysis of these data, while protecting respondent confidentiality, has proved challenging to address. In this paper we will focus on the remote analysis approach, under which a confidential dataset is held in a secure environment under the direct control of the data custodian agency. A computer system within the secure environment accepts a query from an analyst, runs it on the data, then returns the results to the analyst. In particular, the analyst does not have direct access to the data at all, and cannot view any microdata records. We further focus on the fitting of linear regression models to confidential data in the presence of outliers and influential points, such as are often present in business data. We propose a new method for protecting confidentiality in linear regression via a remote analysis system, that provides additional confidentiality protection for outliers and influential points in the data. The method we describe in this paper was designed for the prototype DataAnalyser system developed by the Australian Bureau of Statistics, however the method would be suitable for similar remote analysis systems.
Databáze: OpenAIRE
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