Privacy-Preserving Belief Propagation and Sampling
Abstract
We provide provably privacy-preserving versions of belief propagation, Gibbs sampling, and other local algorithms — distributed multiparty protocols in which each party or vertex learns only its final local value, and absolutely nothing else.
Cite
Text
Kearns et al. "Privacy-Preserving Belief Propagation and Sampling." Neural Information Processing Systems, 2007.Markdown
[Kearns et al. "Privacy-Preserving Belief Propagation and Sampling." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/kearns2007neurips-privacypreserving/)BibTeX
@inproceedings{kearns2007neurips-privacypreserving,
title = {{Privacy-Preserving Belief Propagation and Sampling}},
author = {Kearns, Michael and Tan, Jinsong and Wortman, Jennifer},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {745-752},
url = {https://mlanthology.org/neurips/2007/kearns2007neurips-privacypreserving/}
}