Work

Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality

Year: 2007

Type: report

Abstract: : To protect the confidentiality of survey respondents ’ identities and sensi-tive attributes, statistical agencies can release data in which confidential values are replaced with multiple imputations. These are called synthetic data. We propose a two-stage approach to generating synthetic data that enables agencies to release different numbers of imputations for different variables. Generation in two stages can reduce computational burdens, decrease disclosure risk, and increase inferen-tial accuracy relative to generation in one stage. We present methods for obtaining inferences from such data. We describe the application of two stage synthesis to creating a public use file for a German business database.

Source: Social Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences)

Authors: Jerome P. Reiter, Jörg Drechsler

Institution: Duke University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Award: ITR-(ECS+ASE)-(dmc+int): Info Tech Challenges for Secure Access to Confidential Social Science Data

OpenAlex ID: W2133550617

Cites: 35

Cited by: 37

Related to: 10