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Perturbed robust linear estimating equations for confidentiality protection in remote analysis


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.

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This work was done partly while Christine O’Keefe was on secondment to the Australian Bureau of Statistics, and partly while Soonmin Kwon and Soomin Song were Industrial Trainees in CSIRO. The authors thank Mark Westcott for reviewing an earlier draft and prompting a correction. The authors also thank the anonymous referee for comments and questions that have led to an improved exposition and paper. Views expressed in this paper are those of the authors and do not necessarily represent those of the Australian Bureau of Statistics. Where quoted or used, they should be attributed clearly to the authors.

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Correspondence to Christine M. O’Keefe.

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O’Keefe, C.M., Ayre, T., Lucie, S. et al. Perturbed robust linear estimating equations for confidentiality protection in remote analysis. Stat Comput 27, 775–787 (2017).

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  • Business data
  • Linear regression
  • Robust regression
  • Remote access
  • Virtual Data Centre