A compressive multi-kernel method for privacy-preserving machine learning.
Thee ChanyaswadJ. Morris ChangSun-Yuan KungPublished in: IJCNN (2017)
Keyphrases
- privacy preserving
- kernel methods
- machine learning
- learning problems
- privacy preserving data mining
- privacy preservation
- kernel function
- vertically partitioned data
- kernel matrix
- support vector machine
- support vector
- multi party
- similarity function
- reproducing kernel hilbert space
- feature space
- scalar product
- sensitive information
- privacy concerns
- private data
- private information
- data privacy
- secure multiparty computation
- preserving privacy
- privacy preserving association rule mining
- privacy protection
- sensitive data
- data sets
- privacy sensitive
- data mining
- privacy guarantees
- feature selection
- learning algorithm
- partitioned data
- naive bayesian classification
- privacy issues
- privacy requirements
- data perturbation
- homomorphic encryption
- decision trees
- real world
- random projections