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Activity Number: 511
Type: Contributed
Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #313782 View Presentation
Title: Descriptive and Analytic Inferences from Input and Output De-Identified Data
Author(s): Avinash Singh*+ and Yongheng Lin and Joshua Borton
Companies: NORC at the University of Chicago and NORC at the University of Chicago and NORC at the University of Chicago
Keywords: Input X-ID ; Output X-ID ; Resampling Methods
Abstract:

Query-based systems for disclosure-safe analysis without having direct access to the raw data housed behind firewalls are gaining popularity as an alternative to the traditional PUFs. NORC X-ID system for data de-identification provides two such tools-Input X-ID and Output X-ID. The Input X-ID treats the raw data for disclosure without using the traditional framework of quasi-identifying and sensitive variables. Through a query tool, it can provide summary statistics for small or micro-groups of individuals which are further subject to uncertainty by subsampling and calibration; and thus avoids the limitations of traditional PUFs due to potential increase in disclosure risk with the dynamic nature of intruder knowledge. The Output X-ID avoids the limitations of traditional remote analysis servers due to differncing attacks by potential disclosure. It imposes pre-specified analysis domain-level restrictions through a checklist. We show how both descriptive and analytic inferences can be conducted for X-ID data with generally simple modifications of existing procedures.


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