Utility-Embraced Microaggregation for Machine Learning Applications

Utility-Embraced Microaggregation for Machine Learning Applications

With access to vast amounts of data, privacy protection is more important than ever. Among various de-identification (anonymization) techniques, <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-anonymous microaggregation has been widely s...

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Bibliographic Details
Main Authors: Soobin Lee, Won-Yong Shin
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Clustering
data utility
dimensionality reduction
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-anonymity
microaggregation
Online Access:https://ieeexplore.ieee.org/document/9796546/

Internet

https://ieeexplore.ieee.org/document/9796546/

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