Title
Estimating Risks Of Identification Disclosure In Partially Synthetic Data
Abstract
To limit disclosures, statistical agencies and other data disseminators can release partially synthetic, public use microdata sets. These comprise the units originally surveyed, but some collected values, for example sensitive values at high risk of disclosure or values of key identifiers, are replaced with multiple draws from statistical models. Because the original records are on the file, there remain risks of identifications. In this paper, we describe how to evaluate identification disclosure risks in partially synthetic data, accounting for released information from the multiple datasets, the model used to generate synthetic values, and the approach used to select values to synthesize. We illustrate the computations using the Survey of Youths in Custody.
Year
Keywords
Field
2009
confidentiality,record linkage
Data science,Record linkage,Data mining,Confidentiality,Identifier,Synthetic data,Microdata (HTML),Statistical model,Engineering
DocType
Citations 
PageRank 
Journal
20
1.87
References 
Authors
4
2
Name
Order
Citations
PageRank
Jerome P. Reiter121622.12
Robin Mitra2283.26