On Using Linear Diophantine Equations to Tune the extent of Look Ahead
  while Hiding Decision Tree Rules
  This paper focuses on preserving the privacy of sensitive pat-terns when
inducing decision trees. We adopt a record aug-mentation approach for hiding
sensitive classification rules in binary datasets. Such a hiding methodology is
preferred over other heuristic solutions like output perturbation or
crypto-graphic techniques - which restrict the usability of the data - since
the raw data itself is readily available for public use. In this paper, we
propose a look ahead approach using linear Diophantine equations in order to
add the appropriate number of instances while minimally disturbing the initial
entropy of the nodes.
