Privately computing a distributed k-nn classifier

Authors:
Murat Kantarcioglu;Chris Clifton
Affiliations:
Purdue University, West Lafayette, IN;Purdue University, West Lafayette, IN
Venue:
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2004

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Abstract

The ability of databases to organize and share data often raises privacy concerns. Data warehousing combined with data mining, bringing data from multiple sources under a single authority, increases the risk of privacy violations. Privacy preserving data mining provides a means of addressing this issue, particularly if data mining is done in a way that doesnt disclose information beyond the result. This paper presents a method for privately computing k-nn classification from distributed sources without revealing any information about the sources or their data, other than that revealed by the final classification result.