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Big Data Warehouse for Healthcare-Sensitive Data Applications.

Arsalan Shahid1, Thien-An Ngoc Nguyen1, M-Tahar Kechadi1

  • 1School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland.

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|April 3, 2021
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Summary
This summary is machine-generated.

The BigO project uses big data and sensor technology to model childhood obesity prevalence and predict policy impacts. It prioritizes data security and privacy for vulnerable populations through a layered architecture and privacy-aware protocols.

Keywords:
big data representationbig data securityhealthcare dataprivacy-aware models

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Area of Science:

  • Public Health
  • Data Science
  • Information Security

Background:

  • Childhood obesity is a significant global public health issue requiring effective interventions.
  • Monitoring and evaluating behavioral changes for obesity interventions is challenging.
  • The Big Data against Childhood Obesity (BigO) project addresses these challenges using large-scale data and sensor technologies.

Purpose of the Study:

  • To develop comprehensive, data-driven models for childhood obesity prevalence.
  • To enable data-driven predictions on the impact of community policies.
  • To provide real-time population monitoring and data analysis for obesity interventions.

Main Methods:

  • Implementation of a three-layered data warehouse architecture (back-end, access control, controller).
  • Focus on data access control, secure storage, anonymization, and de-identification of personal data.
  • Development and integration of privacy-aware data analysis protocols.

Main Results:

  • A robust system architecture for handling large-scale sensitive data from children.
  • Implementation of role-based permissions and secured views for data access control.
  • Demonstration of privacy-preserving data analysis techniques for obesity modeling.

Conclusions:

  • The BigO system effectively integrates privacy-aware protocols for secure data management in childhood obesity research.
  • The proposed architecture ensures data security and privacy for a vulnerable population.
  • Big data analytics and sensor technology offer promising avenues for addressing childhood obesity.