L. Sweeney. International Journal on Uncertainty Fuzziness and Knowledge-based Systems, 5 (10):
557--570(2002)issn = 0218-4885,
publisher = World Scientific Publishing Co., Inc.,
address = River Edge, NJ, USA,.
Abstract
Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful? The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. This paper also examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected. The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, µ-Argus and k-Similar provide guarantees of privacy protection.
%0 Journal Article
%1 paper:sweeney:2002
%A Sweeney, Lantanya
%D 2002
%J International Journal on Uncertainty Fuzziness and Knowledge-based Systems
%K 2002 anonymity privacy to-read
%N 10
%P 557--570
%T k-anonymity: a model for protecting privacy
%U http://portal.acm.org/citation.cfm?id=774552#
%V 5
%X Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful? The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. This paper also examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected. The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, µ-Argus and k-Similar provide guarantees of privacy protection.
@article{paper:sweeney:2002,
abstract = {Consider a data holder, such as a hospital or a bank, that has a privately held collection of person-specific, field structured data. Suppose the data holder wants to share a version of the data with researchers. How can a data holder release a version of its private data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful? The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment. A release provides k-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. This paper also examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected. The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, µ-Argus and k-Similar provide guarantees of privacy protection. },
added-at = {2008-11-17T12:40:32.000+0100},
author = {Sweeney, Lantanya},
biburl = {https://www.bibsonomy.org/bibtex/2873ad7ef58cc028e8b8b02e4589d4b4c/mschuber},
interhash = {710b9ae24ee9fdee57033bd50346dbe6},
intrahash = {873ad7ef58cc028e8b8b02e4589d4b4c},
journal = {International Journal on Uncertainty Fuzziness and Knowledge-based Systems},
keywords = {2002 anonymity privacy to-read},
note = { issn = {0218-4885},
publisher = {World Scientific Publishing Co., Inc.},
address = {River Edge, NJ, USA},
},
number = 10,
pages = {557--570},
timestamp = {2008-11-17T12:40:32.000+0100},
title = {k-anonymity: a model for protecting privacy},
url = {http://portal.acm.org/citation.cfm?id=774552#},
volume = 5,
year = 2002
}