L. Sweeney. International Journal on Uncertainty, Fuzziness and Knowledge-based
Systems, 10 (5):
557--570(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.
%0 Journal Article
%1 Swee02-kmp
%A Sweeney, L.
%D 2002
%J International Journal on Uncertainty, Fuzziness and Knowledge-based
Systems
%K Privacy data_anonymity data_fusion datenbank deanonymisierung masterarbeit re-identification
%N 5
%P 557--570
%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{Swee02-kmp,
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,
{{\textmu}-Argus} and {k-Similar} provide guarantees of privacy protection.},
added-at = {2010-06-13T21:13:32.000+0200},
author = {Sweeney, L.},
biburl = {https://www.bibsonomy.org/bibtex/233681de5cff0f42542893556d69dc730/pilpul},
description = {Masterarbeit},
interhash = {710b9ae24ee9fdee57033bd50346dbe6},
intrahash = {33681de5cff0f42542893556d69dc730},
journal = {International Journal on Uncertainty, Fuzziness and Knowledge-based
Systems},
keywords = {Privacy data_anonymity data_fusion datenbank deanonymisierung masterarbeit re-identification},
number = 5,
pages = {557--570},
timestamp = {2010-06-13T21:13:37.000+0200},
title = {{k-anonymity: a model for protecting privacy.}},
volume = 10,
year = 2002
}