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Related Concept Videos

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Related Experiment Video

Updated: Jan 17, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Privacy-Conducive Data Ecosystem Architecture: By-Design Vulnerability Assessment Using Privacy Risk Expansion Factor

Ionela Chereja1, Rudolf Erdei1, Daniela Delinschi1

  • 1Department of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
Summary

New metrics, the privacy risk expansion factor (PREF) and privacy exposure index (PEI), assess how data architecture impacts sensitive data exposure. This aids in developing privacy-first data ecosystems.

Keywords:
NewSQLcybersecuritydata protectiondata vulnerabilityecosystem architecturemulti-layer data warehousing architectureprivacy by designprivacy exposure indexprivacy risk expansion factorprivacy-first database systems

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Area of Science:

  • Computer Science
  • Information Security
  • Data Privacy

Background:

  • Growing data ecosystems necessitate robust privacy risk assessment.
  • Existing methodologies may not fully capture architectural impacts on data exposure.

Purpose of the Study:

  • Introduce and validate novel metrics for privacy risk assessment.
  • Evaluate the influence of architectural decisions on sensitive data exposure and distribution.

Main Methods:

  • Developed two complementary metrics: Privacy Risk Expansion Factor (PREF) and Privacy Exposure Index (PEI).
  • Applied metrics to representative use cases for validation.

Main Results:

  • Metrics provide structured insights into the privacy impact of architectural choices.
  • Demonstrated comparative analysis capabilities across different data architectures.

Conclusions:

  • The proposed metrics support the development of privacy-first data ecosystems.
  • Establishes a foundation for future research in AI-driven privacy risk monitoring.