Privately Computing a Distributed k-nn Classifier

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Author

Christopher Clifton

Tech report number

CERIAS TR 2004-92

Entry type

conference

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 kâ€“nn classification from distributed sources without revealing any information about the sources or their data, other than that revealed by the final classification result.

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Date

2004 – 09

URL

http://www.springerlink.com/content/d605hv9708xj8vdk/

Address

Pisa, Italy

Key alpha

Clifton

Note

8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) September 20-24, 2004 in Pisa, Italy

Publication Date

2004-09-01

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@Conference{ Clifton,
	title = "Privately Computing a Distributed k-nn Classifier ",
	author = "Christopher Clifton",
	year = "2004",
	month = "09",
	address = "Pisa, Italy",
	note = "8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)
September 20-24, 2004 in Pisa, Italy",
	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 kâ€“nn classification from distributed sources without revealing any information about the sources or their data, other than that revealed by the final classification result.",
	url = "http://www.springerlink.com/content/d605hv9708xj8vdk/",
}