BMC Med Inform Decis Mak - A method for managing re-identification risk from small geographic areas in Canada.

Tópicos

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Resumo

CKGROUND: 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 (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).RESULTS: 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.CONCLUSION: 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.

Resumo Limpo

ckground common disclosur control practic health dataset identifi small geograph area either suppress record small area aggreg larger one recent studi provid method decid area small base uniqu criterion uniqu criterion stipul area longer small proport uniqu individu relev variabl quasiidentifi approach zero howev use uniqu valu zero quit stringent threshold suitabl risk data disclosur quit high uniqu threshold propos health data method estim uniqu urban forward sortat area fsas use long form canadian census data repres popul construct two logist regress model predict uniqu greater threshold valid predict accuraci use fold crossvalid predictor variabl includ popul size fsa maximum number possibl valu quasiidentifi number equival classesresult model paramet signific model high predict accuraci specif sensit threshold model respect applic model illustr analysi ontario newborn registri emerg depart dataset higher threshold consider fewer record compar threshold consid small area therefor undergo disclosur control action also includ concret guidanc data custodian decid one three uniqu threshold use depend mitig control data recipi place potenti invas privaci data disclos motiv capac data recipi reidentifi dataconclus model develop can use manag reidentif risk small geograph area abl choos among three possibl threshold data custodian can adjust definit small geograph area natur data recipi

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