Convex optimization-based Privacy-Preserving Distributed Least Squares via Subspace Perturbation.
Qiongxiu LiRichard HeusdensMads Græsbøll ChristensenPublished in: EUSIPCO (2020)
Keyphrases
- privacy preserving
- convex optimization
- least squares
- horizontally partitioned data
- horizontally partitioned
- data perturbation
- multi party
- partitioned data
- privacy preserving data mining
- privacy sensitive
- privacy guarantees
- privacy preservation
- interior point methods
- vertically partitioned data
- privacy preserving classification
- secure multiparty computation
- total variation
- primal dual
- preserving privacy
- data privacy
- private information
- low rank
- convex optimization problems
- principal component analysis
- sensitive data
- privacy protection
- peer to peer
- feature space
- privacy concerns
- scalar product
- data mining
- high dimensional data
- low dimensional
- feature extraction