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InfoResearchPeer-reviewed

A New $k$k-Anonymity Method Based on Generalization First $k$k-Member Clustering for Healthcare Data

Source
IEEE Xplore (Security & AI Journals)(opens in a new tab)
Published
Record updated

Summary

Researchers propose Generalization First k-Member Clustering (GFKMC), a new k-anonymity method for healthcare data that applies early generalization to quasi-identifiers. Compared with Mondrian, Top-Down Greedy (TDG), and Clustering-Based (CB) baselines, GFKMC keeps information loss at roughly 25% across varying k values. It also better preserves machine learning model performance, especially at low k.