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Non-homogeneous Generalization in Privacy Preserving Data Publishing

Summary: Proposes non-homogeneous generalization for k-anonymity, reducing utility loss by varying quasi-identifiers inside partitions. Offers verification, a randomized defense against algorithm-aware attacks, and a partitioning technique that boosts data utility. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4290
Venue
SIGMOD
Year
2010
Pagerank
4.1945683e-05
Overall Rank
12,229 | 14.93%
DOI
-

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