A compressive multi-kernel method for privacy-preserving machine learning.
Thee ChanyaswadJ. Morris ChangSun-Yuan KungPublished in: CoRR (2021)
Keyphrases
- privacy preserving
- kernel methods
- machine learning
- learning problems
- privacy preserving data mining
- privacy preservation
- support vector machine
- vertically partitioned data
- support vector
- similarity function
- privacy sensitive
- multi party
- scalar product
- kernel function
- sensitive information
- privacy preserving association rule mining
- kernel matrix
- data mining
- feature space
- reproducing kernel hilbert space
- private data
- private information
- secure multiparty computation
- data privacy
- high dimensional feature space
- privacy concerns
- decision trees
- differential privacy
- privacy requirements
- privacy protection
- partitioned data
- sensitive data
- privacy issues
- privacy guarantees
- data perturbation
- training data
- homomorphic encryption
- learning algorithm
- random projections