Quantifying the Vulnerability of Attributes for Effective Privacy Preservation Using Machine Learning

Quantifying the Vulnerability of Attributes for Effective Privacy Preservation Using Machine Learning

Personal data have been increasingly used in data-driven applications to improve quality of life. However, privacy preservation of personal data while sharing it with analysts/ researchers has become an essential requirement to be met by data owners (hospitals, banks, insurance companies, etc.). The...

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Bibliographic Details
Main Authors: Abdul Majeed, Seong Oun Hwang
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Personal data
privacy
utility
anonymization
vulnerability
machine learning
Online Access:https://ieeexplore.ieee.org/document/10011406/

Internet

https://ieeexplore.ieee.org/document/10011406/

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