A Privacy Assessment Framework for Data Tiers in Multilayered Ecosystem Architectures
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 met...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/7/1116 |
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| author | Ionela Chereja Rudolf Erdei Emil Pasca Daniela Delinschi Anca Avram Oliviu Matei |
| author_facet | Ionela Chereja Rudolf Erdei Emil Pasca Daniela Delinschi Anca Avram Oliviu Matei |
| author_sort | Ionela Chereja |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2f0fc14a5d724145a2a45f76752b69a9 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-2f0fc14a5d724145a2a45f76752b69a92025-08-20T02:15:58ZengMDPI AGMathematics2227-73902025-03-01137111610.3390/math13071116A Privacy Assessment Framework for Data Tiers in Multilayered Ecosystem ArchitecturesIonela Chereja0Rudolf Erdei1Emil Pasca2Daniela Delinschi3Anca Avram4Oliviu Matei5Department of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, RomaniaDepartment of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, RomaniaDepartment of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, RomaniaDepartment of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, RomaniaDepartment of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, RomaniaDepartment of Electrical, Electronics and Computer Engineering, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, RomaniaData-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.https://www.mdpi.com/2227-7390/13/7/1116privacy compliancedata privacyethical AI under GDPRmachine learningmulti-layer data warehousing architectureecosystem architecture |
| spellingShingle | Ionela Chereja Rudolf Erdei Emil Pasca Daniela Delinschi Anca Avram Oliviu Matei A Privacy Assessment Framework for Data Tiers in Multilayered Ecosystem Architectures Mathematics privacy compliance data privacy ethical AI under GDPR machine learning multi-layer data warehousing architecture ecosystem architecture |
| title | A Privacy Assessment Framework for Data Tiers in Multilayered Ecosystem Architectures |
| title_full | A Privacy Assessment Framework for Data Tiers in Multilayered Ecosystem Architectures |
| title_fullStr | A Privacy Assessment Framework for Data Tiers in Multilayered Ecosystem Architectures |
| title_full_unstemmed | A Privacy Assessment Framework for Data Tiers in Multilayered Ecosystem Architectures |
| title_short | A Privacy Assessment Framework for Data Tiers in Multilayered Ecosystem Architectures |
| title_sort | privacy assessment framework for data tiers in multilayered ecosystem architectures |
| topic | privacy compliance data privacy ethical AI under GDPR machine learning multi-layer data warehousing architecture ecosystem architecture |
| url | https://www.mdpi.com/2227-7390/13/7/1116 |
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