Estimating risks of identification disclosure in microdata
成果类型：
Article
署名作者：
Reiter, JP
署名单位：
Duke University
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1198/016214505000000619
发表日期：
2005
页码：
1103-1112
关键词：
摘要：
When statistical agencies release microdata to the public, malicious users (intruders) may be able to link records in the released data to records in external databases. Releasing data in ways that fail to prevent such identifications may discredit the agency or, for some data, constitute a breach of law. To limit disclosures, agencies often release altered versions of the data; however, there usually remain risks of identification. This article applies and extends the framework developed by Duncan and Lambert for computing probabilities of identification for sampled units. It describes methods tailored specifically to data altered by recoding and topcoding variables, data swapping, or adding random noise (and combinations of these common data alteration techniques) that agencies can use to assess threats from intruders who possess information on relationships among variables and the methods of data alteration. Using data from the Current Population Survey, the article illustrates a step-by-step process for evaluating identification disclosure risks for competing releases under varying assumptions of intruders' knowledge. Risk measures are presented for individual units and for entire datasets.
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