Faculdade Engenharia
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Browsing Faculdade Engenharia by Sustainable Development Goals (SDG) "10:Reduzir as Desigualdades"
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- Matrices - Book of Abstracts: II International Congress Architecture and GenderPublication . Pedrosa, Patrícia Alexandra Dias Santos ; Santos, Eliana Sousa; Matos, Maria João Pereira de ; Alvarez Lombardero, NuriaThe Second International Congress on Architecture and Gender will address the theme of Matrices. This concept has several definitions and they are all inclusive by nature. Matrices are environments where things develop, the models or patterns that shape formations, and they can also reinvent an environment. These images are suited to address the current patterns of change regarding architecture and gender.
- Subtle biases introduced in equity studies through data anonymizationPublication . Fazendeiro, Paulo; Prata, Paula; Ferrão, Maria Eugénia; Altman, MicahThis 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 undermine the research's objective of promoting 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.