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.
National Institute of Standards and Technology (U.S.)
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
E-GOVERNMENT SEBAGAI LAYANAN KOMUNIKASI PEMERINTAH KOTA SURABAYA (Studi Kematangan e-government Sebagai Layanan Komunikasi Government to Government, Government to Citizen, Government to Business)
E-GOVERNMENT SEBAGAI LAYANAN KOMUNIKASI PEMERINTAH KOTA SURABAYA (Studi Kematangan e-government Sebagai Layanan Komunikasi Government to Government, Government to Citizen, Government to Business)
Fenomena pergeseran media komunikasi kearah IoT (Internet of Things) sebagai salah satu model komunikasi saat ini harus disikapi positif oleh pemerintah daerah untuk bisa berinova...
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...
An Exploration of the Relationship between Government Type and Bureaucratic Structural Reorganisation in New Zealand, 1957–2017
An Exploration of the Relationship between Government Type and Bureaucratic Structural Reorganisation in New Zealand, 1957–2017
<p><b>The type of government, whether the cabinet is a single-party majority, multiparty coalition, minority, or oversized, is often claimed to be one important factor ...
Privacy risk quantification in education data using Markov model
Privacy risk quantification in education data using Markov model
AbstractWith Big Data revolution, the education sector is being reshaped. The current data‐driven education system provides many opportunities to utilize the enormous amount of col...
Open Source Community Portals for E-Government
Open Source Community Portals for E-Government
The value of the Internet as a flexible tool for the posting and exchange of information is expressed in the potential it has for governance, commerce, and social interaction. The ...
Open Source Community Portals for E-Government
Open Source Community Portals for E-Government
The value of the Internet as a flexible tool for the posting and exchange of information is expressed in the potential it has for governance, commerce, and social interaction. The ...
MANAGEMENT OF STATE LOCAL GOVERNMENT JOINT ACCOUNT AND LOCAL GOVERNMENT SERVICE DELIVERY IN GOMBE STATE (2013-2018)
MANAGEMENT OF STATE LOCAL GOVERNMENT JOINT ACCOUNT AND LOCAL GOVERNMENT SERVICE DELIVERY IN GOMBE STATE (2013-2018)
It was difficult for the Federal government to administer successfully all the functions of government through the central organ of the state. It is on this account that local gove...
Cooperation strategy of government-enterprise supply chain finance based on differential game
Cooperation strategy of government-enterprise supply chain finance based on differential game
PurposeThe government plays an important role in the financing process of small and medium enterprises (SMEs), but the current government-enterprise cooperation (GEC) mechanism can...

