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Updated: May 24, 2026

A Familiarization Protocol Facilitates the Participation of Children with ASD in Electrophysiological Research
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Published on: July 31, 2017

Profile-Aware Consent Management: A Conceptual Framework.

Anastasia Terzi1, Panos Bonotis1, Stamatia Bibi1

  • 1Department of Electrical and Computer Engineering, University of Western Macedonia.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Managing personal data consent is difficult. This study proposes a profile-aware framework using AI to personalize consent, adapting to user needs and regulations for better data management.

Keywords:
Dynamic consent managementGDPRaimachine learninguser profiling

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Last Updated: May 24, 2026

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08:42

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Published on: July 31, 2017

Area of Science:

  • Computer Science
  • Information Science
  • Data Privacy

Background:

  • Current user consent management is often static and struggles with diverse user needs, evolving preferences, and regulatory shifts.
  • Challenges persist across healthcare, digital services, and research sectors in effectively managing personal data consent.
  • Existing mechanisms lack personalization and adaptability, leading to compliance and transparency issues.

Purpose of the Study:

  • To introduce a conceptual framework for Profile-aware Consent Management within the CONSENT project.
  • To address the limitations of static consent mechanisms by developing a dynamic and personalized approach.
  • To enhance user experience and maintain regulatory compliance in personal data consent processes.

Main Methods:

  • Leveraging user profiling, adaptive form generation, and automated consenting agents.
  • Gathering multi-stakeholder insights from clinicians, citizens, IT managers, and GDPR experts.
  • Translating identified pain points in current consent workflows into technical design requirements.

Main Results:

  • A conceptual framework for Profile-aware Consent Management has been developed.
  • The framework integrates AI for dynamic adaptation of consent presentation and decisions.
  • Design requirements are informed by real-world stakeholder challenges and regulatory needs.

Conclusions:

  • The proposed AI-powered, profile-aware system offers a personalized and simplified consent process.
  • Dynamic adaptation of consent presentation and decisions enhances user needs fulfillment.
  • The framework provides a robust backbone for regulatory compliance and transparency in data management.