Further Insights: Balancing Privacy, Explainability, and Utility in Machine Learning-based Tabular Data Analysis.
Wisam AbbasiPaolo MoriAndrea SaracinoPublished in: ARES (2024)
Keyphrases
- machine learning
- data analysis
- data mining
- differential privacy
- privacy requirements
- privacy preserving
- knowledge discovery
- data anonymization
- business intelligence
- data processing
- active learning
- privacy aware
- high dimensional data
- data collection
- personal information
- pattern recognition
- utility function
- privacy protection
- natural language processing
- privacy issues
- private data
- model selection
- text mining
- security issues
- data publishing
- anonymized data
- cluster analysis
- privacy preservation
- private information
- disclosure risk
- artificial intelligence
- natural language
- machine learning methods
- information extraction
- learning systems
- supervised learning
- text classification
- machine learning algorithms
- reinforcement learning
- support vector machine
- security risks
- computational intelligence
- knowledge acquisition
- computer vision
- inductive learning
- personal data
- life sciences
- privacy concerns
- feature selection
- statistical databases
- sensitive information
- learning tasks