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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Sistema mejorado de almacenamiento seguro y gestión de la privacidad de los datos para grandes volúmenes de datos

Tang Ting1, Ming Li2

  • 1School of General Education, Sichuan Vocational and Technical College, Suining, 629000, Sichuan, China. tang.ting1970@outlook.com.

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Este estudio presenta un modelo de almacenamiento seguro en la nube de múltiples capas (MLSCSM) para proteger los datos confidenciales del personal en los sistemas de big data. El modelo mejora la seguridad y la eficiencia en entornos de nube.

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Área de la Ciencia:

  • Ciencias de la computación
  • Seguridad de los datos
  • La computación en la nube

Sus antecedentes:

  • La gestión y seguridad de datos sensibles a gran escala, en particular los registros de personal, es un desafío importante en entornos en la nube.
  • La creciente complejidad y escala de los grandes sistemas de datos requieren soluciones de seguridad avanzadas.

Objetivo del estudio:

  • Proponer un nuevo modelo de almacenamiento seguro en la nube multicapa (MLSCSM) para datos de personal a gran escala en entornos en la nube.
  • Integrar métodos criptográficos y estadísticos para la preservación de la privacidad y el almacenamiento seguro de datos.

Principales métodos:

  • El MLSCSM combina el cifrado ChaCha20, el particionamiento de datos de doble etapa (DSDP), la k-anonimización, el hashing SHA-512 y la dispersión basada en la matriz de Cauchy.
  • Los bloques de datos están codificados de forma segura, enmascarados y distribuidos en múltiples plataformas en la nube en función de varios factores.
  • El modelo incorpora registros de auditoría, balance de carga y evaluación de recursos en tiempo real.

Principales resultados:

  • El modelo propuesto logró un tiempo de codificación de 250 ms (tamaño de bloque 75), un uso de CPU del 23% para datos de 256 MB y una baja latencia de 14 ms.
  • Demostró un alto rendimiento de hasta 139 ms, superando a los modelos de referencia como RDFA, SDPMC y P&XE.
  • La validación con el conjunto de datos MIMIC-III en un clúster Hadoop confirmó la eficacia del sistema.

Conclusiones:

  • El MLSCSM ofrece seguridad superior, eficiencia y escalabilidad para aplicaciones de almacenamiento de big data basadas en la nube.
  • La integración de técnicas criptográficas y estadísticas proporciona una solución sólida para la gestión de datos que preserva la privacidad.
  • El modelo está optimizado para entornos de computación en nube distribuidos (CCE).