2007
NeurIPS
NeurIPS 2007
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.
🌱
Topic Pioneer
- Knowledge Graphs
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Interdisciplinary Bridge
- Artificial Intelligence and Computer Science and Security & Privacy
📈
Trend Setter
- Information Retrieval
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Keyword Pioneer
- scientific discovery
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Hot Topic Early Bird
- scientific discovery
Authors
Topics
Artificial Intelligence > Core AI > Multi-Agent Systems
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Optimization & Theory > Distributed Learning
Machine Learning > Optimization & Theory > Stochastic Processes
Machine Learning > Application Areas > Privacy
Computer Science > Applications > Information Retrieval
Security & Privacy > Privacy
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Inference
Artificial Intelligence > Core AI > Knowledge Graphs
Machine Learning > Bayesian & Probabilistic > Markov Chain Monte Carlo
Keywords
belief propagation
privacy preservation
dense retrieval
citation analysis
privacy preserving
gibbs sampling
distributed inference
graphical model
local algorithm
distributed algorithm
privacy-preserving algorithm
scientific discovery
methodology inspiration retrieval
llm-based reranking
methodology adjacency graph
multiparty protocol