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Activity Number: 550 - Differential Privacy in Statistical Agencies: Present and Future
Type: Invited
Date/Time: Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
Sponsor: Government Statistics Section
Abstract #322231 View Presentation
Title: Differential Private Sign and Significance for Regression Coefficients
Author(s): Andres Felipe Barrientos* and Jerry Reiter and Ashwin Machanavajjhala and Yan Chen
Companies: Duke University and Department of Statistical Science, Duke University and Duke University and Duke University
Keywords: Confidentiality ; Data utility ; Synthetic data ; Verification servers

Datasets containing sensitive information, particularly about single individuals, are easy to find in different contexts. This information has to be protected, which often implies the dataset cannot be released. Limited access to datasets affects those analysts interested in global statistics, but not necessarily in single records. In this work we present two protected inferential procedures for private datasets in the linear regression context. Specifically, we describe differential private procedures for the sign and significance of regression coefficients. Procedures satisfying the differential privacy property allow releasing global statistics while controlling the amount of sensitive information that could be disclosed. The procedures are designed to make model-based inferences in finite and infinite population settings. The proposal combines subsample and aggregate methods, Laplace mechanism and t-statistics. We assess the performance of our proposal through analyses on simulated and real-life datasets.

Authors who are presenting talks have a * after their name.

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