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- W4382201991 abstract "The increasing need for data privacy and the rising complexity of data environments necessitate robust data anonymization techniques to safeguard personal and sensitive information. A multi-model approach to data anonymization can strike an optimal balance between privacy protection and data utility, integrating techniques such as data masking, differential privacy, machine learning algorithms, blockchain technology, and data encryption. This article introduces a Security-Centric Enterprise Data Anonymization Governance Model, a structured framework for managing data privacy across healthcare, finance, and government industries. The model ensures adherence to best practices and compliance with legal and regulatory requirements. The article addresses challenges in implementing data anonymization techniques, including maintaining data utility and preventing re-identification, by advocating for a multi-model approach that combines various technologies and methods. We suggest that by adopting this holistic approach, organizations can enhance their data protection measures and foster a culture of data privacy." @default.
- W4382201991 created "2023-06-28" @default.
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- W4382201991 date "2023-04-15" @default.
- W4382201991 modified "2023-09-25" @default.
- W4382201991 title "An Enterprise Data Privacy Governance Model: Security-Centric Multi-Model Data Anonymization" @default.
- W4382201991 doi "https://doi.org/10.29137/umagd.1272085" @default.
- W4382201991 hasPublicationYear "2023" @default.
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