Hiding Decision Tree Rules in Medical Data: A Case Study

Data sharing among health organizations has become an increasingly common process, but any organization will most likely try to hide some sensitive patterns before it shares its data with others. This article focuses on the protection of sensitive patterns when we assume that decision trees will be the models to be induced. We apply a heuristic approach to hideany arbitrary rule from the derivation of a binary decision tree. The proposed hiding method is preferred over other heuristic solutions such as output disturbance or encryption methods that limit data usability, as the raw data itself can then more easily be offered for access by any third parties.