Privacy-Conducive Data Ecosystem Architecture: By-Design Vulnerability Assessment Using Privacy Risk Expansion Factor and Privacy Exposure Index
Ionela Chereja, Rudolf Erdei, Daniela Delinschi, Emil Pasca, Anca Avram, Oliviu Matei

Abstract. Modern data ecosystems integrate heterogeneous sources, processing layers and stakeholders, making the by-design assessment of privacy vulnerabilities particularly challenging. This paper proposes a Privacy-Conducive Data Ecosystem Architecture that supports by-design vulnerability assessment using two novel metrics: the Privacy Risk Expansion Factor and the Privacy Exposure Index. The expansion factor quantifies how privacy risks propagate through different architectural layers and integration points, while the exposure index aggregates these risks into a single, interpretable score. The architecture and metrics are validated on representative IoT and smart agriculture scenarios, demonstrating their usefulness for designing ecosystems that are inherently more privacy-respectful.

Keywords: privacy by design; data ecosystem; vulnerability assessment; privacy risk; IoT; smart agriculture

Ionela Chereja, Rudolf Erdei, Daniela Delinschi, Emil Pasca, Anca Avram, Oliviu Matei, "Privacy-Conducive Data Ecosystem Architecture: By-Design Vulnerability Assessment Using Privacy Risk Expansion Factor and Privacy Exposure Index", Sensors (Basel), vol. 25, no. 11, MDPI, 2025, 2023. DOI: https://doi.org/10.3390/s25113554.

Reference: Sensors (Basel), vol. 25, no. 11, MDPI, 2025. DOI: 10.3390/s25113554

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