This repository contains the collected resources submitted to and created by the NIST Collaborative Research Cycle (CRC) Data and Metrics Archive. The NIST Collaborative Research Cycle (CRC) is an ongoing effort to benchmark, compare, and investigate deidentification technologies. The program asks the research community to deidentify a compact and interesting dataset called the NIST Diverse Communities Data Excerpts, demographic data from communities across the U.S. sourced from the American Community Survey. This repository contains all of the submitted deidentified data instances each accompanied by a detailed abstract describing how the deidentified data were generated. We conduct an extensive standardized evaluation of each deidentified instance using a host of fidelity, utility, and privacy metrics, using out tool, SDNist. We?ve packaged the data, abstracts, and evaluation results into a human- and machine-readable archive.
About this Dataset
| Title | NIST Collaborative Research Cycle Data and Metrics Archive |
|---|---|
| Description | This repository contains the collected resources submitted to and created by the NIST Collaborative Research Cycle (CRC) Data and Metrics Archive. The NIST Collaborative Research Cycle (CRC) is an ongoing effort to benchmark, compare, and investigate deidentification technologies. The program asks the research community to deidentify a compact and interesting dataset called the NIST Diverse Communities Data Excerpts, demographic data from communities across the U.S. sourced from the American Community Survey. This repository contains all of the submitted deidentified data instances each accompanied by a detailed abstract describing how the deidentified data were generated. We conduct an extensive standardized evaluation of each deidentified instance using a host of fidelity, utility, and privacy metrics, using out tool, SDNist. We?ve packaged the data, abstracts, and evaluation results into a human- and machine-readable archive. |
| Modified | 2024-02-13 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | Privacy , deidentified data , synthetic data , differential privacy , benchmarks |
{
"identifier": "ark:\/88434\/mds2-3024",
"accessLevel": "public",
"contactPoint": {
"hasEmail": "mailto:[email protected]",
"fn": "Gary Howarth II"
},
"programCode": [
"006:045"
],
"landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-3024",
"title": "NIST Collaborative Research Cycle Data and Metrics Archive",
"description": "This repository contains the collected resources submitted to and created by the NIST Collaborative Research Cycle (CRC) Data and Metrics Archive. The NIST Collaborative Research Cycle (CRC) is an ongoing effort to benchmark, compare, and investigate deidentification technologies. The program asks the research community to deidentify a compact and interesting dataset called the NIST Diverse Communities Data Excerpts, demographic data from communities across the U.S. sourced from the American Community Survey. This repository contains all of the submitted deidentified data instances each accompanied by a detailed abstract describing how the deidentified data were generated. We conduct an extensive standardized evaluation of each deidentified instance using a host of fidelity, utility, and privacy metrics, using out tool, SDNist. We?ve packaged the data, abstracts, and evaluation results into a human- and machine-readable archive.",
"language": [
"en"
],
"distribution": [
{
"accessURL": "https:\/\/github.com\/usnistgov\/privacy_collaborative_research_cycle",
"format": "Git repository",
"description": "Deidentified data submitted to the CRC and their evaluation results.",
"title": "Collaborative Research Cycle Data and Metrics Archive"
}
],
"bureauCode": [
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],
"modified": "2024-02-13 00:00:00",
"publisher": {
"@type": "org:Organization",
"name": "National Institute of Standards and Technology"
},
"theme": [
"Information Technology:Data and informatics",
"Information Technology:Software research",
"Information Technology:Privacy"
],
"keyword": [
"Privacy",
"deidentified data",
"synthetic data",
"differential privacy",
"benchmarks"
]
}
