Privacy-Preserving Distributed k-Anonymity

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Author

Christopher Clifton

Tech report number

CERIAS TR 2005-134

Entry type

conference

Abstract

k-anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k-anonymous data, the model has been that of a single source wanting to publish data. This paper presents a k-anonymity protocol when the data is vertically partitioned between sites. A key contribution is a proof that the protocol preserves k-anonymity between the sites: While one site may have individually identifiable data, it learns nothing that violates k-anonymity with respect to the data at the other site. This is a fundamentally different distributed privacy definition than that of Secure Multiparty Computation, and it provides a better match with both ethical and legal views of privacy.

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Date

2005 – 08

URL

http://www.springerlink.com/content/3348r6au680u6n49/

Address

Storrs, Connecticut

Key alpha

Clifton

Note

The 19th Annual IFIP WG 11.3 Working Conference on Data and Applications Security August 7-10, 2005

Publication Date

2005-08-01

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@Conference{ Clifton,
	title = "Privacy-Preserving Distributed k-Anonymity",
	author = "Christopher Clifton",
	year = "2005",
	month = "08",
	address = "Storrs, Connecticut",
	note = "The 19th Annual IFIP WG 11.3 Working Conference on Data and Applications Security
August 7-10, 2005",
	abstract = "k-anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k-anonymous data, the model has been that of a single source wanting to publish data. This paper presents a k-anonymity protocol when the data is vertically partitioned between sites. A key contribution is a proof that the protocol preserves k-anonymity between the sites: While one site may have individually identifiable data, it learns nothing that violates k-anonymity with respect to the data at the other site. This is a fundamentally different distributed privacy definition than that of Secure Multiparty Computation, and it provides a better match with both ethical and legal views of privacy.",
	url = "http://www.springerlink.com/content/3348r6au680u6n49/",
}