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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
4352
Venue
SIGMOD
Year
2010
Pagerank
5.093636e-05
Overall Rank
12,424 | 14.77%
DOI
10.1145/1807167.1807248

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Authors

BibTeX Citation

@inproceedings{wong_sigmod10,
        title = {{Non-homogeneous Generalization in Privacy Preserving Data Publishing}},
        author = {Wong, W. K. and Mamoulis, Nikos and Cheung, David W.},
        series = {{SIGMOD} '10},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1807167.1807248},
        url = {https://dl.acm.org/doi/10.1145/1807167.1807248},
        year = {2010}
}

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