Hiding decision tree rules by data set operations

This paper focuses on preserving the privacy of sensitive patterns in the context of inducing decision trees. The subject at hand is approached through a record augmentation approach for hiding sensitive classification rules in binary datasets. Such a hiding methodology is preferred over other heuristic solutions like output perturbation or cryptographic techniques - that restrict the usability of the data in different ways - since the raw data itself is readily available for public use. This methodology is based upon the unique characteristics of the induction of binary decision trees with binary-valued symbolic attributes and binary classes.