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Protecting respondents’ identities in microdata release (2001)
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| Venue: | In IEEE Transactions on Knowledge and Data Engineering (TKDE |
| Citations: | 511 - 32 self |
BibTeX
@INPROCEEDINGS{Samarati01protectingrespondents’,
author = {Pierangela Samarati},
title = {Protecting respondents’ identities in microdata release},
booktitle = {In IEEE Transactions on Knowledge and Data Engineering (TKDE},
year = {2001},
pages = {1010--1027}
}
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Abstract
Today’s globally networked society places great demand on the dissemination and sharing of information. While in the past released information was mostly in tabular and statistical form, many situations call today for the release of specific data (microdata). In order to protect the anonymity of the entities (called respondents) to which information refers, data holders often remove or encrypt explicit identifiers such as names, addresses, and phone numbers. De-identifying data, however, provides no guarantee of anonymity. Released information often contains other data, such as race, birth date, sex, and ZIP code, that can be linked to publicly available information to re-identify respondents and inferring information that was not intended for disclosure. In this paper we address the problem of releasing microdata while safeguarding the anonymity of the respondents to which the data refer. The approach is based on the definition of k-anonymity. A table provides k-anonymity if attempts to link explicitly identifying information to its content map the information to at least k entities. We illustrate how k-anonymity can be provided without compromising the integrity (or truthfulness) of the information released by using generalization and suppression techniques. We introduce the concept of minimal generalization that captures the property of the release process not to distort the data more than needed to achieve k-anonymity, and present an algorithm for the computation of such a generalization. We also discuss possible preference policies to choose among different minimal generalizations. Index terms:
Keyphrases
respondent identity microdata release index term release process suppression technique possible preference policy phone number zip code specific data available information information refers networked society place great demand data holder explicit identifier different minimal generalization de-identifying data data refer minimal generalization many situation statistical form birth date
