L. Sweeney. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10 (5):
557-570(2002)
DOI: 10.1142/S0218488502001648
Аннотация
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 DBLP:journals/ijufks/Sweene02
%A Sweeney, Latanya
%D 2002
%J International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
%K k-anonymity
%N 5
%P 557-570
%R 10.1142/S0218488502001648
%T k-Anonymity: A Model for Protecting Privacy
%V 10
%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{DBLP:journals/ijufks/Sweene02,
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 = {2010-02-16T07:06:21.000+0100},
author = {Sweeney, Latanya},
bibsource = {DBLP, http://dblp.uni-trier.de},
biburl = {https://www.bibsonomy.org/bibtex/2e11a2af10b11e589d557d03e23400555/ytyoun},
doi = {10.1142/S0218488502001648},
interhash = {710b9ae24ee9fdee57033bd50346dbe6},
intrahash = {e11a2af10b11e589d557d03e23400555},
journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},
keywords = {k-anonymity},
number = 5,
pages = {557-570},
timestamp = {2016-06-14T14:06:13.000+0200},
title = {k-Anonymity: A Model for Protecting Privacy},
volume = 10,
year = 2002
}