On the Relations between Retention Replacement, Additive Perturbation, and Randomisations for Nominal Attributes in Privacy Preserving Data Mining.
Piotr AndruszkiewiczPublished in: ISMIS (2012)
Keyphrases
- privacy preserving data mining
- data perturbation
- nominal attributes
- privacy preserving
- attribute values
- data mining
- data privacy
- statistical databases
- data mining techniques
- numerical attributes
- numeric attributes
- data mining algorithms
- random projections
- privacy concerns
- subgroup discovery
- machine learning
- sparse representation
- nearest neighbor
- training data
- databases