A Privacy Assessment Framework for Data Tiers in Multilayered Ecosystem Architectures

Ionela Chereja (), Rudolf Erdei, Emil Pasca, Daniela Delinschi, Anca Avram and Oliviu Matei
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Ionela Chereja: Department of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
Rudolf Erdei: Department of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
Emil Pasca: Department of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
Daniela Delinschi: Department of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
Anca Avram: Department of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
Oliviu Matei: Department of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania

Mathematics, 2025, vol. 13, issue 7, 1-19

Abstract: Data-centric operational systems, machine learning (ML), and other analytical and artificial intelligence (AI) pipelines are becoming increasingly imperative for organizations seeking to increase the protection of sensitive data while satisfying customer expectations. This paper proposes a novel methodology to assess the level of vulnerability assigned to each of the data storage components in complex multilayered data ecosystems through a nuanced assessment of data persistence and content metrics. The suggested methodology introduces a new and effective way to address the issues of determining perceived privacy risk across data storage layers and informing necessary security measures for an ecosystem by calculating an ecosystem vulnerability score. This offers a comprehensive overview of data vulnerability, aiding in the identification of high-risk components and guiding strategic decisions for enhancing data privacy and security measures. With consistent and generalized assessment of risk, the methodology can properly pinpoint the most vulnerable storage systems and assist in directing efforts to mitigate them.

Keywords: privacy compliance; data privacy; ethical AI under GDPR; machine learning; multi-layer data warehousing architecture; ecosystem architecture; cybersecurity; data protection; data vulnerability; data risk assessment; ecosystem vulnerability score (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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