A method for managing re-identification risk from small geographic areas in Canada.

ABSTRACT:

Background

A common disclosure control practice for health datasets is to identify small geographic areas and either suppress records from these small areas or aggregate them into larger ones. A recent study provided a method for deciding when an area is too small based on the uniqueness criterion. The uniqueness criterion stipulates that an the area is no longer too small when the proportion of unique individuals on the relevant variables (the quasi-identifiers) approaches zero. However, using a uniqueness value of zero is quite a stringent threshold, and is only suitable when the risks from data disclosure are quite high. Other uniqueness thresholds that have been proposed for health data are 5% and 20%.

Methods

We estimated uniqueness for urban Forward Sortation Areas (FS

SUBMITTER: El Emam K 

PROVIDER: S-EPMC2858714 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature