ADAPTIVE AND AUTOMATED DATA ANONYMIZATION FRAMEWORKS FOR MULTI-CLOUD ENVIRONMENTS
Keywords:
Data Anonymization, Multi-Cloud Security, Privacy Framework, Automated Data Masking, Regulatory ComplianceAbstract
This technical article explores the implementation of adaptive and automated data anonymization frameworks across diverse computing environments, focusing on mainframe, SAP ERP, and cloud platforms. The article examines current data security challenges, core anonymization techniques, including K-anonymity, L-diversity, and T-closeness, and the Delphix implementation framework for automated data masking. The article investigates platform-specific considerations, benefits of automation, and comprehensive monitoring systems while providing insights into immediate improvements and future developments. The findings demonstrate that robust anonymization frameworks significantly enhance data protection, operational efficiency, and compliance management across multi-cloud environments.
References
P. Ohm, "Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization," 2010. Available: https://www.uclalawreview.org/pdf/57-6-3.pdf
El Emam, K., & Arbuckle, L., "Anonymizing Health Data: Case Studies and Methods to Get You Started," 2013. Available: https://www.oreilly.com/library/view/anonymizing-health-data/9781449363062/
Sweeney, L., "Protecting Privacy when Disclosing Information: k-Anonymity and Its Enforcement through Generalization and Suppression," 2002. Available: https://dataprivacylab.org/projects/kanonymity/paper3.pdf
Duncan, G. T., Elliot, M., & Salazar-González, J. J., "Statistical Confidentiality: Principles and Practice," 2011. Available: https://link.springer.com/book/10.1007/978-1-4419-7802-8
Li, N., Li, T., & Venkatasubramanian, S., "t-Closeness: Privacy Beyond k-Anonymity and l-Diversity," 2007. Available: https://ieeexplore.ieee.org/document/4221659
Fung, B. C., Wang, K., Chen, R., & Yu, P. S., "Privacy-preserving data publishing: A survey of recent developments," 2010. Available: https://dl.acm.org/doi/10.1145/1749603.1749605
Wells, D. et al., "Make Data Compliance Easier Across Your Enterprise," Delphix Blog, 2021. Available: https://www.delphix.com/blog/make-data-compliance-easier-across-your-enterprise
SAP HANA Cloud, "SAP HANA Database Administration Guide: Data Anonymization," SAP Help Portal, 2024. Available: https://help.sap.com/docs/hana-cloud-database/sap-hana-cloud-sap-hana-database-administration-guide/data-anonymization
Wilfred Raj, H., et al., "A Survey of Data Anonymization Techniques for Privacy-Preserving Mining in Bigdata," 2020. Available: https://thescipub.com/pdf/jcssp.2020.194.201.pdf
Shanika Wickramasinghe, "What's Data Masking? Types, Techniques & Best Practices," 2021. Available: https://www.bmc.com/blogs/data-masking/
Narayanan, A., & Shmatikov, V., "Robust de-anonymization of large datasets (how to break anonymity of the Netflix prize dataset)," 2008. Available: https://ieeexplore.ieee.org/document/4531148
Zarsky, T. Z., "Incompatible: The GDPR in the Age of Big Data," 2016. Available: https://scholarship.shu.edu/shlr/vol47/iss4/2
ISO/IEC, "ISO/IEC 20889:2018: Privacy-enhancing data de-identification terminology and classification of techniques," 2019. Available: https://www.iso.org/standard/69373.html
Dwork, C., & Roth, A., "The Algorithmic Foundations of Differential Privacy," 2014. Available: https://www.nowpublishers.com/article/Details/TCS-042
Voigt, P., & Von dem Bussche, A., "The EU General Data Protection Regulation (GDPR): A Practical Guide," 2017. Available: https://link.springer.com/book/10.1007/978-3-319-57959-7
Garfinkel, S., & Leclerc, P., "De-identification of Personal Information," 2015. Available: https://nvlpubs.nist.gov/nistpubs/ir/2015/NIST.IR.8053.pdf
K. Gangapatnam, "Federated Learning for Cross Cloud System Migrations: A Privacy-Preserving Approach," 2024. Available: https://ijsrcseit.com/index.php/home/article/view/CSEIT241061161/CSEIT241061161
K. Gangapatnam, “Automated Cloud Migration Strategies for Enterprise Resource Planning Systems: A Technical Analysis” 2024, Available: https://iaeme.com/MasterAdmin/Journal_uploads/IJCET/VOLUME_15_ISSUE_6/IJCET_15_06_080.pdf
