Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation.
Qiongxiu LiJaron Skovsted GundersenKatrine TjellRafal WisniewskiMads Græsbøll ChristensenPublished in: CoRR (2022)
Keyphrases
- privacy preserving
- gaussian mixture model
- expectation maximization
- em algorithm
- horizontally partitioned data
- data perturbation
- horizontally partitioned
- mixture model
- partitioned data
- multi party
- privacy preserving data mining
- privacy sensitive
- privacy guarantees
- feature space
- privacy preservation
- probabilistic model
- maximum likelihood
- vertically partitioned data
- privacy preserving classification
- gaussian mixture
- data privacy
- secure multiparty computation
- probability density function
- feature vectors
- private information
- privacy protection
- density estimation
- privacy concerns
- unsupervised learning
- sensitive information
- privacy preserving association rule mining
- distributed environment
- differential privacy
- generative model
- preserving privacy
- mixture of gaussians
- image segmentation
- private data
- gaussian model
- sensitive data
- low dimensional
- privacy requirements
- dimensionality reduction
- principal component analysis
- high dimensional
- machine learning
- computer vision
- decision trees
- bayesian networks
- scalar product
- k means
- distributed data