(<italic>r, k, ε</italic>)-Anonymization: Privacy-Preserving Data Publishing Algorithm Based on Multi-Dimensional Outlier Detection, <italic>k</italic>-Anonymity, and <italic>ε</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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Detaylı Bibliyografya
Asıl Yazarlar:	Burak Cem Kara, Can Eyupoglu, Oktay Karakus
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	IEEE 2025-01-01
Seri Bilgileri:	IEEE Access
Konular:	Privacy-preserving data publishing data anonymity ε-differential privacy k-anonymity
Online Erişim:	https://ieeexplore.ieee.org/document/10960292/
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

(<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

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