Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes

Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes

The publication of a patient’s dataset is essential for various medical investigations and decision-making. Currently, significant focus has been established to protect privacy during data publishing. The existing privacy models for multiple sensitive attributes do not concentrate on the...

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Bibliographic Details
Main Authors: J. Jayapradha, M. Prakash, Youseef Alotaibi, Osamah Ibrahim Khalaf, Saleh Ahmed Alghamdi
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Privacy-preserving
anatomization
heap bucketization
Pearson correlation
k-anonymity
slicing
Online Access:https://ieeexplore.ieee.org/document/9732456/

Internet

https://ieeexplore.ieee.org/document/9732456/

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