Centroid inter-cluster distance.
<div><p>This work investigates the trade-off between data anonymization and utility, particularly focusing on the implications for equity-related research in education. Using microdata from the 2019 Brazilian National Student Performance Exam (ENADE), the study applies the (ε, δ)-Differe...
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2025
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| _version_ | 1852015945803890688 |
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| author | Paulo Fazendeiro (12233780) |
| author2 | Paula Prata (12233777) Maria Eugénia Ferrão (12233774) |
| author2_role | author author |
| author_facet | Paulo Fazendeiro (12233780) Paula Prata (12233777) Maria Eugénia Ferrão (12233774) |
| author_role | author |
| dc.creator.none.fl_str_mv | Paulo Fazendeiro (12233780) Paula Prata (12233777) Maria Eugénia Ferrão (12233774) |
| dc.date.none.fl_str_mv | 2025-10-08T17:25:18Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0332441.t002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Centroid_inter-cluster_distance_/30308359 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Ecology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified students &# 8217 involving domain experts dataset &# 8217 subtle biases introduced group categories related driven policies aimed prevent anonymization efforts educational equity analysis studies aimed related research introducing biases educational equity xlink "> work investigates using microdata study concludes study applies sociodemographic variables results reveal research evaluates minority groups may jeopardise finding highlights equity studies enade ), economic inequalities could undermine careful attention anonymized datasets anonymization techniques anonymization process also lead |
| dc.title.none.fl_str_mv | Centroid inter-cluster distance. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>This work investigates the trade-off between data anonymization and utility, particularly focusing on the implications for equity-related research in education. Using microdata from the 2019 Brazilian National Student Performance Exam (ENADE), the study applies the (ε, δ)-Differential Privacy model to explore the impact of anonymization on the dataset’s utility for socio-educational equity analysis. By clustering both the original and anonymized datasets, the research evaluates how group categories related to students’ sociodemographic variables, such as gender, race, income, and parental education, are affected by the anonymization process. The results reveal that while anonymization techniques can preserve overall data structure, they can also lead to the suppression or misrepresentation of minority groups, introducing biases that may jeopardise the promotion of educational equity. This finding highlights the importance of involving domain experts in the interpretation of anonymized data, particularly in studies aimed at reducing socio-economic inequalities. The study concludes that careful attention is needed to prevent anonymization efforts from distorting key group categories, which could undermine the validity of data-driven policies aimed at promoting equity.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_6cf9b2376b50bd2a9a6772bdf0c9dec4 |
| identifier_str_mv | 10.1371/journal.pone.0332441.t002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30308359 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Centroid inter-cluster distance.Paulo Fazendeiro (12233780)Paula Prata (12233777)Maria Eugénia Ferrão (12233774)EcologyScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedstudents &# 8217involving domain expertsdataset &# 8217subtle biases introducedgroup categories relateddriven policies aimedprevent anonymization effortseducational equity analysisstudies aimedrelated researchintroducing biaseseducational equityxlink ">work investigatesusing microdatastudy concludesstudy appliessociodemographic variablesresults revealresearch evaluatesminority groupsmay jeopardisefinding highlightsequity studiesenade ),economic inequalitiescould underminecareful attentionanonymized datasetsanonymization techniquesanonymization processalso lead<div><p>This work investigates the trade-off between data anonymization and utility, particularly focusing on the implications for equity-related research in education. Using microdata from the 2019 Brazilian National Student Performance Exam (ENADE), the study applies the (ε, δ)-Differential Privacy model to explore the impact of anonymization on the dataset’s utility for socio-educational equity analysis. By clustering both the original and anonymized datasets, the research evaluates how group categories related to students’ sociodemographic variables, such as gender, race, income, and parental education, are affected by the anonymization process. The results reveal that while anonymization techniques can preserve overall data structure, they can also lead to the suppression or misrepresentation of minority groups, introducing biases that may jeopardise the promotion of educational equity. This finding highlights the importance of involving domain experts in the interpretation of anonymized data, particularly in studies aimed at reducing socio-economic inequalities. The study concludes that careful attention is needed to prevent anonymization efforts from distorting key group categories, which could undermine the validity of data-driven policies aimed at promoting equity.</p></div>2025-10-08T17:25:18ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0332441.t002https://figshare.com/articles/dataset/Centroid_inter-cluster_distance_/30308359CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303083592025-10-08T17:25:18Z |
| spellingShingle | Centroid inter-cluster distance. Paulo Fazendeiro (12233780) Ecology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified students &# 8217 involving domain experts dataset &# 8217 subtle biases introduced group categories related driven policies aimed prevent anonymization efforts educational equity analysis studies aimed related research introducing biases educational equity xlink "> work investigates using microdata study concludes study applies sociodemographic variables results reveal research evaluates minority groups may jeopardise finding highlights equity studies enade ), economic inequalities could undermine careful attention anonymized datasets anonymization techniques anonymization process also lead |
| status_str | publishedVersion |
| title | Centroid inter-cluster distance. |
| title_full | Centroid inter-cluster distance. |
| title_fullStr | Centroid inter-cluster distance. |
| title_full_unstemmed | Centroid inter-cluster distance. |
| title_short | Centroid inter-cluster distance. |
| title_sort | Centroid inter-cluster distance. |
| topic | Ecology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified students &# 8217 involving domain experts dataset &# 8217 subtle biases introduced group categories related driven policies aimed prevent anonymization efforts educational equity analysis studies aimed related research introducing biases educational equity xlink "> work investigates using microdata study concludes study applies sociodemographic variables results reveal research evaluates minority groups may jeopardise finding highlights equity studies enade ), economic inequalities could undermine careful attention anonymized datasets anonymization techniques anonymization process also lead |