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Assessing Re-identification Risk in Sample Microdata

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DLAW03 - New developments in data privacy

Co-author: Chris Skinner      

Abstract:    Disclosure risk occurs when there is a high probability that an   intruder can identify an individual in released sample microdata and confidential information may be revealed. A probabilistic modelling  framework based on the Poisson log-linear model is used for  quantifying disclosure risk in terms of population uniqueness when  population counts are unknown. This method does not account for  measurement error arising either naturally from survey processes or  purposely introduced as a perturbative disclosure limitation technique. The probabilistic modelling framework for assessing disclosure risk is  expanded to take into account the misclassification/ perturbation and  demonstrated on sample microdata which has undergone   perturbation  procedures. Finally, we adapt the probabilistic modelling framework to   assess the disclosure risk of samples from sub-populations  and show some initial results.

This talk is part of the Isaac Newton Institute Seminar Series series.

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