Adaptive Anonymity via $b$-Matching
Abstract
The adaptive anonymity problem is formalized where each individual shares their data along with an integer value to indicate their personal level of desired privacy. This problem leads to a generalization of $k$-anonymity to the $b$-matching setting. Novel algorithms and theory are provided to implement this type of anonymity. The relaxation achieves better utility, admits theoretical privacy guarantees that are as strong, and, most importantly, accommodates a variable level of anonymity for each individual. Empirical results confirm improved utility on benchmark and social data-sets.
Cite
Text
Choromanski et al. "Adaptive Anonymity via $b$-Matching." Neural Information Processing Systems, 2013.Markdown
[Choromanski et al. "Adaptive Anonymity via $b$-Matching." Neural Information Processing Systems, 2013.](https://mlanthology.org/neurips/2013/choromanski2013neurips-adaptive/)BibTeX
@inproceedings{choromanski2013neurips-adaptive,
title = {{Adaptive Anonymity via $b$-Matching}},
author = {Choromanski, Krzysztof M and Jebara, Tony and Tang, Kui},
booktitle = {Neural Information Processing Systems},
year = {2013},
pages = {3192-3200},
url = {https://mlanthology.org/neurips/2013/choromanski2013neurips-adaptive/}
}