Local Distortion Hiding in Financial Technology application: a case study with a benchmark data set

Data sharing has become an increasingly common procedure among financial institutions, but any organisation will most probably attempt to conceal some critical rules before exchange their information with others. This paper concentrates on protecting sensitive rules when we assume that binary, decision trees will be the models to be induced by the shared data. The suggested heuristic hiding technique is preferred over other heuristic solutions such as output disturbance or encryption methods that restrict data usability, as the raw data itself can then more easily be offered for access by any third parties. In this article, we present a paradigm of using the Local Distortion Hiding (LDH) algorithm in a real-life financial data set to hide a sensitive rule.