JSM 2004 - Toronto

Abstract #300055

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Activity Number: 418
Type: Invited
Date/Time: Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
Sponsor: National Institute of Statistical Sciences
Abstract - #300055
Title: Disclosure Risk and Data Utility for Remote-access Regression Servers
Author(s): Jerome Reiter*+ and Ashish Sanil and Alan F. Karr and Shanti Gomatam
Companies: Duke University and National Institute of Statistical Sciences and National Institute of Statistical Sciences and U.S. Food and Drug Administration
Address: ISDS, Durham, NC, 27708,
Keywords: confidentiality ; diagnostic ; disclosure ; regression ; remote access
Abstract:

Given the public's ever increasing concerns about data confidentiality, in the near future statistical agencies may not be willing, or may not be legally allowed, to release any genuine microdata (data on individual units). In such a world, one microdata dissemination strategy is remote-access computer servers, to which users submit requests for output from statistical models fit using the collected data, but they are not allowed access to the genuine data. Remote servers, however, are not free from the risks of unintentional data disclosures. This paper describes these risks, and it suggests quantifiable measures of risk and data utility that can be used to specify which queries can be answered with output. This risk-utility framework is illustrated for regression models using simulated data. Methods for releasing safe and useful model diagnostics are also discussed.

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