Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning.
Artur FilipowiczThee ChanyaswadSun-Yuan KungPublished in: CoRR (2017)
Keyphrases
- machine learning
- learning algorithm
- machine learning algorithms
- learning tasks
- high dimensional data
- low dimensional
- computational intelligence
- privacy preserving
- text mining
- personal information
- security issues
- privacy protection
- active learning
- privacy preserving data mining
- compressive sensing
- data analysis
- sampling rate
- pattern recognition
- security risks
- data mining
- artificial intelligence
- private data
- inductive logic programming
- privacy enhancing
- statistical methods
- semi supervised learning
- knowledge acquisition
- information extraction
- high dimensional
- feature space
- supervised learning
- sensitive information
- private information
- explanation based learning
- support vector machine
- computer science
- reinforcement learning
- feature extraction
- legal issues
- computer vision