JSM 2005 - Toronto

Abstract #302413

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Legend: = Applied Session, = Theme Session, = Presenter
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 - #302413
Title: Privacy Preserving Statistical Analysis for Horizontally Partitioned Datasets
Author(s): Xiaodong Lin*+
Companies: University of Cincinnati/NISS
Address: 817 Old Chemistry, Department of Mathematics, Cincinnati, OH, 45242,
Keywords:
Abstract:

Information sharing is critical in the effort to coordinate database owners. Privacy and security considerations can prevent such processes, thus derailing further statistical analysis projects. In this talk, we will introduce several secure multiparty computation protocols and show how to build complex privacy preserving statistical analysis models using them. We will discuss privacy preserving regression and record linkage procedures for the case when datasets are horizontally partitioned, assuming the participating parties are "semihonest." The applications of these procedures for the data integration of federal agencies and large corporations also will be discussed.

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