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Personal data privacy is fundamental in human activity and health monitoring systems, with additional challenges posed by the integration of AI tools. For monitoring to be effective, the user needs to trust on the system, adopt and use it frequently. Besides data privacy requirements and regulatory compliance, transparency, explainability and accountability matter. By incorporating Privacy by Design principles into AI-driven systems to ensure GDPR alignment, this paper proposed a simple approach for embedding privacy-preserving mechanisms throughout the data lifecycle, from design to deployment and continuous monitoring and illustrate it with two use cases developing AI-Driven Systems for human activity and health monitoring in different contexts.

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Integrating Privacy by Design Principles into AI-Driven Systems for Human Activity and Health Monitoring

  • 会议论文

  • 发表日期：

    2025年

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  • Rute Almeida,
  • Alberto Freitas,
  • Teresa Silva,
  • Duarte Dias,
  • Joyca Lacroix,
  • Ignace De Lathauwer,
  • Goreti Marreiros,
  • Luís Conceição

摘要

Personal data privacy is fundamental in human activity and health monitoring systems, with additional challenges posed by the integration of AI tools. For monitoring to be effective, the user needs to trust on the system, adopt and use it frequently. Besides data privacy requirements and regulatory compliance, transparency, explainability and accountability matter. By incorporating Privacy by Design principles into AI-driven systems to ensure GDPR alignment, this paper proposed a simple approach for embedding privacy-preserving mechanisms throughout the data lifecycle, from design to deployment and continuous monitoring and illustrate it with two use cases developing AI-Driven Systems for human activity and health monitoring in different contexts.