Perturbação Controlada de Autovetores em PCA para Preservação de Privacidade em Dados Sensíveis

• Ivo A. Pimenta UECE
• Kaynan S. Freitas UECE
• Evellin S. Moura UECE
• Erick S. Nascimento UECE
• Fabio A. Faria IST / Ulisboa
• Rafael L. Gomes UECE

DOI: https://doi.org/10.5753/sbrc.2026.19276

Resumo

A crescente adoção de soluções digitais baseadas em dados de usuários requer mecanismos robustos de privacidade. Perturbações excessivas podem degradar significativamente o desempenho de modelos de Aprendizado de Máquina. Neste trabalho, propomos o PerturbPCA-α, uma abordagem ajustável baseada em PCA. A técnica introduz perturbação contínua nos autovetores principais do conjunto de dados. Isso permite um controle explícito do trade-off entre privacidade e utilidade. Consequentemente, a capacidade de aprendizado dos modelos de ML é mantida, conforme demonstrado por experimentos com dados de saúde que validam a efetividade do método proposto.

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