JSM 2005 Online Program

Abstract #302456

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:	173
Type:	Invited
Date/Time:	Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor:	National Institute of Statistical Sciences
Abstract - #302456
Title:	Privacy-Preserving Statistical Analyses of Distributed Data Using Data Perturbations
Author(s):	Ashish P. Sanil*+
Companies:	National Institute of Statistical Sciences
Address:	19 TW Alexandrer Drive, RTP, NC, 27709-4006,
Keywords:
Abstract:	Owners of databases containing similar or related data often would like to perform statistical analyses of the combined data in order to obtain more informative insights into the data. However, there usually are several barriers to straightforward data pooling and analysis, such as concerns about proprietary nature of that data and the privacy of the data subjects. In this paper, we describe a novel data perturbation technique that can be used to distort the data to the extent required for the masking of the data records but at the same time preserve desirable properties that permit us to exactly replicate several kinds of statistical analyses (e.g., linear regression and density estimation). We then show how this masking method can be used by multiple data owners to perform statistical analyses on their combined data without revealing individual data records. Algorithms and other implementation issues also are discussed.

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