Table servers protect confidentiality in tabular data releases
Communications of the ACM
Model Diagnostics for Remote Access Regression Servers
Statistics and Computing
Remote access systems for statistical analysis of microdata
Statistics and Computing
Computer Methods and Programs in Biomedicine
A Remote Analysis Server - What Does Regression Output Look Like?
PSD '08 Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases
An investigation of model-based microdata masking for magnitude tabular data release
PSD'12 Proceedings of the 2012 international conference on Privacy in Statistical Databases
Confidentialising maps of mixed point and diffuse spatial data
PSD'12 Proceedings of the 2012 international conference on Privacy in Statistical Databases
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This paper is concerned with the problem of balancing the competing objectives of allowing statistical analysis of confidential data while maintaining standards of privacy and confidentiality. Remote analysis servers have been proposed as a way to address this problem by delivering results of statistical analyses without giving the analyst any direct access to data. Several national statistical agencies operate remote analysis servers [Australian Bureau of Statistics Remote Access Data Laboratory (RADL), ; Luxembourg Income Study, ]. Remote analysis servers are not free from disclosure risk, and current implementations address this risk by ''confidentialising'' the underlying data and/or by denying some queries. In this paper we explore the alternative solution of ''confidentialising'' the output of a server so that no confidential information is revealed or can be inferred. In this paper we review results on remote analysis servers, and provide a list of measures for confidentialising the output from a single regression query to a remote server as developed by Sparks et al. [R. Sparks, C. Carter, J. Donnelly, J. Duncan, C.M. O'Keefe, L. Ryan, A framework for performing statistical analyses of unit record health data without violating either privacy or confidentiality of individuals, in: Proceedings of the 55th Session of the International Statistical Institute, Sydney, 2005; R. Sparks, C. Carter, J. Donnelly, C.M. O'Keefe, J. Duncan, T. Keighley, D. McAullay, Remote access methods for exploratory data analysis and statistical modelling: privacy-preserving Analytics^(TM), Comput. Meth. Prog. Biomed. 91 (2008) 208-222.] We provide a fully worked example, and compare the confidentialised output from the query with the output from a traditional statistical package. Finally, we provide a comparison the confidentialised regression diagnostics with the synthetic regression diagnostics generated by the alternative method of Reiter [J.P. Reiter, Model diagnostics for remote-access regression servers, Statistics and Computing 13 (2003) 371-380].