Tools for privacy preserving distributed data mining

Authors:
Chris Clifton;Murat Kantarcioglu;Jaideep Vaidya;Xiaodong Lin;Michael Y. Zhu
Affiliations:
Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN
Venue:
ACM SIGKDD Explorations Newsletter
Year:
2002

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Abstract

Privacy preserving mining of distributed data has numerous applications. Each application poses different constraints: What is meant by privacy, what are the desired results, how is the data distributed, what are the constraints on collaboration and cooperative computing, etc. We suggest that the solution to this is a toolkit of components that can be combined for specific privacy-preserving data mining applications. This paper presents some components of such a toolkit, and shows how they can be used to solve several privacy-preserving data mining problems.