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A Privacy Assessment Framework for Data Tiers in Multilayered Ecosystem Architectures

Author

Listed:
  • 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
    R&D Department, Holisun, str. Cărbunari nr. 8, 430397 Baia Mare, Romania)

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.

Suggested Citation

  • Ionela Chereja & Rudolf Erdei & Emil Pasca & Daniela Delinschi & Anca Avram & Oliviu Matei, 2025. "A Privacy Assessment Framework for Data Tiers in Multilayered Ecosystem Architectures," Mathematics, MDPI, vol. 13(7), pages 1-19, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:7:p:1116-:d:1622898
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    References listed on IDEAS

    as
    1. Kwangmin Jung, 2021. "Extreme Data Breach Losses: An Alternative Approach to Estimating Probable Maximum Loss for Data Breach Risk," North American Actuarial Journal, Taylor & Francis Journals, vol. 25(4), pages 580-603, November.
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