Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

De-identifying government datasets:

View through CrossRef
De-identification is a general term for any process of removing the association between a set of identifying data and the data subject. This document describes the use of de-identification with the goal of preventing or limiting disclosure risks to individuals and establishments while still allowing for the production of meaningful statistical analysis. Government agencies can use de-identification to reduce the privacy risk associated with collecting, processing, archiving, distributing, or publishing government data. Previously, NIST IR 8053, De-Identification of Personal Information, provided a detailed survey of de-identification and re-identification techniques. This document provides specific guidance to government agencies that wish to use de-identification. Before using de-identification, agencies should evaluate their goals for using de-identification and the potential risks that releasing de-identified data might create. Agencies should decide upon a data-sharing model, such as publishing de-identified data, publishing synthetic data based on identified data, providing a query interface that incorporates de-identification, or sharing data in non-public protected enclaves. Agencies can create a Disclosure Review Board to oversee the process of de-identification. They can also adopt a de-identification standard with measurable performance levels and perform re-identification studies to gauge the risk associated with de-identification. Several specific techniques for de-identification are available, including de-identification by removing identifiers, transforming quasi-identifiers, and generating synthetic data using models. People who perform de-identification generally use special-purpose software tools to perform the data manipulation and calculate the likely risk of re-identification. However, not all tools that merely mask personal information provide sufficient functionality for performing de-identification. This document also includes an extensive list of references, a glossary, and a list of specific de-identification tools, which is only included to convey the range of tools currently available and is not intended to imply a recommendation or endorsement by NIST.
Title: De-identifying government datasets:
Description:
De-identification is a general term for any process of removing the association between a set of identifying data and the data subject.
This document describes the use of de-identification with the goal of preventing or limiting disclosure risks to individuals and establishments while still allowing for the production of meaningful statistical analysis.
Government agencies can use de-identification to reduce the privacy risk associated with collecting, processing, archiving, distributing, or publishing government data.
Previously, NIST IR 8053, De-Identification of Personal Information, provided a detailed survey of de-identification and re-identification techniques.
This document provides specific guidance to government agencies that wish to use de-identification.
Before using de-identification, agencies should evaluate their goals for using de-identification and the potential risks that releasing de-identified data might create.
Agencies should decide upon a data-sharing model, such as publishing de-identified data, publishing synthetic data based on identified data, providing a query interface that incorporates de-identification, or sharing data in non-public protected enclaves.
Agencies can create a Disclosure Review Board to oversee the process of de-identification.
They can also adopt a de-identification standard with measurable performance levels and perform re-identification studies to gauge the risk associated with de-identification.
Several specific techniques for de-identification are available, including de-identification by removing identifiers, transforming quasi-identifiers, and generating synthetic data using models.
People who perform de-identification generally use special-purpose software tools to perform the data manipulation and calculate the likely risk of re-identification.
However, not all tools that merely mask personal information provide sufficient functionality for performing de-identification.
This document also includes an extensive list of references, a glossary, and a list of specific de-identification tools, which is only included to convey the range of tools currently available and is not intended to imply a recommendation or endorsement by NIST.

Related Results

Digital Government in the USA
Digital Government in the USA
Recently, digital government is a prevailing concept in public sectors around the world. Regarding digital governments’ contributions to the democratic administration or democratic...
Review of public motor imagery and execution datasets in brain-computer interfaces
Review of public motor imagery and execution datasets in brain-computer interfaces
The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are avail...
E‐inclusion as a further stage of e‐government?
E‐inclusion as a further stage of e‐government?
PurposeAn IT rationalist discourse predominates in the e‐government literature. Furthermore, and whenever an alternative and holistic discourse is developed, e‐government evaluatio...
Kumpulan Model Maturity E-Government: Sebuah Ulasan Sistematis
Kumpulan Model Maturity E-Government: Sebuah Ulasan Sistematis
AbstrakKemajuan pesat dalam perkembangan teknologi informasi berpengaruh secara global yang dampaknya meluas hampir ke seluruh lini masyarakat. Salah satu bentuk dampak positif dar...
The Optimal Public Expenditure in Developing Countries
The Optimal Public Expenditure in Developing Countries
Many researchers believe that government expenditures promote economic growth at the first development stage. However, as public expenditure becomes too large, countries will suffe...
Challenges and Policy Imperatives for E-Government in Africa
Challenges and Policy Imperatives for E-Government in Africa
Government is a system of social control under which the right to make laws, and the right to enforce them, is vested in a particular group in society. Organizationally, government...
Legal E-Learning and E-Government
Legal E-Learning and E-Government
Today, most e-government Web sites are limited to providing and disseminating legal or legally relevant information (hereafter legal information; see “Key Terms” section). Generall...

Back to Top