Abstract
Quantitative Information Flow assessment of vulnerability for microdata datasets using Bayes Vulnerability.
This tool was used for the vulnerability assessment published in:
• Gabriel H. Nunes - A formal quantitative study of privacy in the publication of official educational censuses in Brazil (2021, hdl:1843/38085).
• Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Carroll Morgan, Gabriel H. Nunes - Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata (2022, DOI: 10.56553/popets-2022-0114, arXiv: 2204.13734). For this publication, also refer to 10.5281/zenodo.6533684 (github.com/nunesgh/inep-anonymization).
We randomly selected only one record for each student with a same unique pseudonymization code (ID_ALUNO) in each dataset. The enrollment code (ID_MATRICULA) for each selected record is available in 10.5281/zenodo.6533675 (gitlab.com/nunesgh/inep-enrollment-codes).
This tool was used for the vulnerability assessment published in:
• Gabriel H. Nunes - A formal quantitative study of privacy in the publication of official educational censuses in Brazil (2021, hdl:1843/38085).
• Mário S. Alvim, Natasha Fernandes, Annabelle McIver, Carroll Morgan, Gabriel H. Nunes - Flexible and scalable privacy assessment for very large datasets, with an application to official governmental microdata (2022, DOI: 10.56553/popets-2022-0114, arXiv: 2204.13734). For this publication, also refer to 10.5281/zenodo.6533684 (github.com/nunesgh/inep-anonymization).
We randomly selected only one record for each student with a same unique pseudonymization code (ID_ALUNO) in each dataset. The enrollment code (ID_MATRICULA) for each selected record is available in 10.5281/zenodo.6533675 (gitlab.com/nunesgh/inep-enrollment-codes).
| Original language | English |
|---|---|
| Media of output | Online |
| Size | 319.5 kB |
| DOIs | |
| Publication status | Published - 28 Apr 2021 |
| Externally published | Yes |