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Perturbed robust linear estimating equations for confidentiality protection in remote analysis

机译：扰动鲁棒线性估计方程，用于远程分析中的机密保护

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摘要

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.
机译：国家统计机构和其他数据保管人收集并持有大量的调查和普查数据，其中包含对研究和政策分析至关重要的信息。然而，事实证明，在保护受访者的机密性的同时，允许对这些数据进行分析的问题颇具挑战性。在本文中，我们将集中于远程分析方法，在这种方法下，机密数据集在数据托管机构的直接控制下被保存在安全的环境中。安全环境中的计算机系统接受分析人员的查询，对数据运行该查询，然后将结果返回给分析人员。特别是，分析人员根本无法直接访问数据，也无法查看任何微数据记录。我们进一步关注在存在异常值和影响点（例如业务数据中经常存在）的情况下，将线性回归模型拟合到机密数据。我们提出了一种通过远程分析系统保护线性回归中的机密性的新方法，该方法为数据中的异常值和影响点提供了额外的机密性保护。我们在本文中描述的方法是为澳大利亚统计局开发的原型DataAnalyser系统设计的，但是该方法将适用于类似的远程分析系统。

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