Privacy-preserving k-NN graphs with autoencoder-based representations for sensitive features (2024)
- Authors:
- USP affiliated authors: PIMENTEL, MARIA DA GRAÇA CAMPOS - ICMC ; MARCACINI, RICARDO MARCONDES - ICMC ; OLIVEIRA, GUSTAVO LIMA DE - ICMC
- Unidade: ICMC
- DOI: 10.5753/eniac.2024.245212
- Subjects: APRENDIZADO COMPUTACIONAL; TEORIA DOS GRAFOS; PRIVACIDADE
- Language: Inglês
- Imprenta:
- Publisher: SBC
- Publisher place: Porto Alegre
- Date published: 2024
- Source:
- Conference titles: Encontro Nacional de Inteligência Artificial e Computacional - ENIAC
- Status:
- Artigo publicado em periódico de acesso aberto (Gold Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
ABNT
OLIVEIRA, Gustavo Lima de e PIMENTEL, Maria da Graça Campos e MARCACINI, Ricardo Marcondes. Privacy-preserving k-NN graphs with autoencoder-based representations for sensitive features. 2024, Anais.. Porto Alegre: SBC, 2024. Disponível em: https://doi.org/10.5753/eniac.2024.245212. Acesso em: 10 abr. 2026. -
APA
Oliveira, G. L. de, Pimentel, M. da G. C., & Marcacini, R. M. (2024). Privacy-preserving k-NN graphs with autoencoder-based representations for sensitive features. In Anais. Porto Alegre: SBC. doi:10.5753/eniac.2024.245212 -
NLM
Oliveira GL de, Pimentel M da GC, Marcacini RM. Privacy-preserving k-NN graphs with autoencoder-based representations for sensitive features [Internet]. Anais. 2024 ;[citado 2026 abr. 10 ] Available from: https://doi.org/10.5753/eniac.2024.245212 -
Vancouver
Oliveira GL de, Pimentel M da GC, Marcacini RM. Privacy-preserving k-NN graphs with autoencoder-based representations for sensitive features [Internet]. Anais. 2024 ;[citado 2026 abr. 10 ] Available from: https://doi.org/10.5753/eniac.2024.245212 - Privacy-preserving heterogeneous network embedding for clinical events
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