(<italic>r, k, &#x03B5;</italic>)-Anonymization: Privacy-Preserving Data Publishing Algorithm Based on Multi-Dimensional Outlier Detection, <italic>k</italic>-Anonymity, and <italic>&#x03B5;</italic>-Differential Privacy

(<italic>r, k, &#x03B5;</italic>)-Anonymization: Privacy-Preserving Data Publishing Algorithm Based on Multi-Dimensional Outlier Detection, <italic>k</italic>-Anonymity, and <italic>&#x03B5;</italic>-Differential Privacy

In recent years, there has been a tremendous rise in both the volume and variety of big data, providing enormous potential benefits to businesses that seek to utilize consumer experiences for research or commercial purposes. The general data protection regulation (GDPR) implementation, on the other...

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Bibliographic Details
Main Authors: Burak Cem Kara, Can Eyupoglu, Oktay Karakus
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Privacy-preserving data publishing
data anonymity
ε-differential privacy
k-anonymity
Online Access:https://ieeexplore.ieee.org/document/10960292/
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https://ieeexplore.ieee.org/document/10960292/

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