JSM 2005 Online Program

Abstract #303608

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	262
Type:	Topic Contributed
Date/Time:	Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Government Statistics
Abstract - #303608
Title:	Preserving Confidentiality While Sharing Vertically Partitioned Data
Author(s):	Christine Kohnen*+ and Jerome Reiter
Companies:	Duke University and Duke University
Address:	Box 90251, Durham, NC, 27708-0352, United States
Keywords:	synthetic data ; disclosure limitation ; multiple imputation ; combining rules ; data sharing
Abstract:	The sharing of data among statistical agencies prior to the application of disclosure limitation methods is restricted due to concerns of confidentiality. Even in situations where agencies are cooperating in a honest environment, the options for the exchange of sensitive data are limited. To circumvent confidentiality constraints, we propose the use of synthetic data methods as a mechanism for secure data sharing and for the creation of public-use data. We focus on the case when agencies own vertically partitioned data: disjoint sets of attributes for the same respondent set. We illustrate the process using data from the 1995 CBECS public-use data file and show how two agencies can share their unperturbed data and create a public-use dataset in the process. We share analyses based on the real data and compare synthetic counterparts along with the disclosure risks involved.

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