2007
NIPS
NeurIPS 2007
Privacy-Preserving Belief Propagation and Sampling
Abstract
We provide provably privacy-preserving versions of belief propagation, Gibbs sampling, and other local algorithms β distributed multiparty protocols in which each party or vertex learns only its ο¬nal local value, and absolutely nothing else.
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Interdisciplinary Bridge
β Artificial Intelligence and Machine Learning
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Trend Setter
β Multi-Agent Systems
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Keyword Pioneer
β privacy-preserving
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Cross-Pollinator
β Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy
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Topic Pioneer
β Knowledge Graphs
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Hot Topic Early Bird
β privacy preservation
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
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Artificial Intelligence > Core AI > Knowledge Graphs
Keywords
belief propagation
privacy preservation
privacy-preserving
dense retrieval
citation analysis
gibbs sampling
distributed inference
graphical model
local algorithm
distributed algorithm
privacy-preserving algorithm
scientific discovery
methodology inspiration retrieval
llm-based reranking
methodology adjacency graph