Data set operations to hide decision tree rules
  This paper focuses on preserving the privacy of sensitive patterns when
inducing decision trees. We adopt 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 - which restrict the usability of the data - since the
raw data itself is readily available for public use. We show some key lemmas
which are related to the hiding process and we also demonstrate the methodology
with an example and an indicative experiment using a prototype hiding tool.
