Name
Abstract | ||
|---|---|---|
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%.We estimated uniqueness for urban Forward Sortation Areas (FSAs) by using the 2001 long form Canadian census data representing 20% of the population. We then constructed two logistic regression models to predict when the uniqueness is greater than the 5% and 20% thresholds, and validated their predictive accuracy using 10-fold cross-validation. Predictor variables included the population size of the FSA and the maximum number of possible values on the quasi-identifiers (the number of equivalence classes).All model parameters were significant and the models had very high prediction accuracy, with specificity above 0.9, and sensitivity at 0.87 and 0.74 for the 5% and 20% threshold models respectively. The application of the models was illustrated with an analysis of the Ontario newborn registry and an emergency department dataset. At the higher thresholds considerably fewer records compared to the 0% threshold would be considered to be in small areas and therefore undergo disclosure control actions. We have also included concrete guidance for data custodians in deciding which one of the three uniqueness thresholds to use (0%, 5%, 20%), depending on the mitigating controls that the data recipients have in place, the potential invasion of privacy if the data is disclosed, and the motives and capacity of the data recipient to re-identify the data.The models we developed can be used to manage the re-identification risk from small geographic areas. Being able to choose among three possible thresholds, a data custodian can adjust the definition of "small geographic area" to the nature of the data and recipient. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1186/1472-6947-10-18 | BMC Med. Inf. & Decision Making |
Keywords | Field | DocType |
data collection,health informatics,population size,risk,disclosure,threshold model,logistic regression model,cross validation | Data science,Data collection,Uniqueness,Data mining,Health informatics,Medicine,Small-Area Analysis | Journal |
Volume | Issue | ISSN |
10 | 1 | 1472-6947 |
Citations | PageRank | References |
13 | 1.23 | 5 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| KHALED EL EMAM | 1 | 2469 | 156.01 |
| Ann Brown | 2 | 13 | 1.23 |
| Philip AbdelMalik | 3 | 14 | 1.59 |
| Angelica Neisa | 4 | 56 | 4.26 |
| Mark Walker | 5 | 16 | 1.66 |
| Jim Bottomley | 6 | 16 | 1.66 |
| Tyson Roffey | 7 | 31 | 2.42 |