Privacy-Preserving Belief Propagation and Sampling

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

Authors

Michael Kearns, Jinsong Tan, Jennifer Wortman

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.