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)
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Authors
- 1. Jianneng Cao (National University of Singapore)
- 2. Panagiotis Karras (Rutgers University)
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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