Privately computing a distributed k-nn classifier

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 doesn't 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. © Springer-Verlag Berlin Heidelberg 2004.