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Publishing Microdata with a Robust Privacy Guarantee

Summary: Introduces β-likeness, bounding each sensitive value’s posterior-confidence increase by a relative threshold—an explicit guarantee absent from prior microdata models. Provides optimized generalization and perturbation anonymizers with low information loss and empirical gains over k-anonymity adaptations. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
10563
Venue
VLDB
Year
2012
Pagerank
5.093636e-05
Overall Rank
12,326 | 15.44%
DOI
10.14778/2350229.2350254

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Authors

BibTeX Citation

@article{cao_vldb12,
        title = {{Publishing Microdata with a Robust Privacy Guarantee}},
        author = {Cao, Jianneng and Karras, Panagiotis},
        journal = {PVLDB},
        series = {{VLDB} '12},
        volume = {5},
        number = {11},
        pages = {1388--1399},
        doi = {10.14778/2350229.2350254},
        url = {https://doi.org/10.14778/2350229.2350254},
        year = {2012}
}

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