September 2024
Intermediate to advanced
366 pages
9h 58m
Spanish
Content preview from Construir Pipelines de Aprendizaje Automático
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Capítulo 14. Privacidad de datos para el aprendizaje automático
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En este capítulo, presentamos algunos aspectos de la privacidad de los datos que se aplican a las cadenas de aprendizaje automático. El aprendizaje automático que preserva la privacidad es un área de investigación muy activa que está empezando a incorporarse a TensorFlow y otros marcos de trabajo. Explicaremos algunos de los principios en los que se basan las técnicas más prometedoras en el momento de escribir estas líneas y mostraremos algunos ejemplos prácticos de cómo pueden encajar en una canalización de aprendizaje automático.
En este capítulo trataremos tres métodos principales para preservar la privacidad en el aprendizaje automático: la privacidad diferencial, el aprendizaje federado y el aprendizaje automático encriptado.
Cuestiones de privacidad de datos
La privacidad de los datos tiene que ver con la confianza y con limitar la exposición de datos que la gente preferiría mantener en privado. Hay muchos métodos diferentes para preservar la privacidad en el aprendizaje automático, y para elegir entre ellos, debes intentar responder a las siguientes preguntas:
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¿De quién intentas mantener la privacidad de los datos?
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¿Qué partes del sistema pueden ser privadas y cuáles pueden exponerse al mundo?
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¿Quiénes son las partes de confianza que pueden ver los datos?
Las respuestas a estas preguntas ...
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