Using Minimum Local Distortion to Hide Decision Tree Rules

Entropy

Georgios Feretzakis¹  Dimitris Kalles¹  VassiliosS. Verykios¹ 

[1] School of Science and Technology, Hellenic Open University, 263 35 Patras, Greece;

关键词: decision trees;    privacy preserving;    hiding rules;    entropy;    information gain;    data sharing;   

DOI  :  10.3390/e21040334

来源: DOAJ

【 摘 要 】

The sharing of data among organizations has become an increasingly common procedure in several areas like banking, electronic commerce, advertising, marketing, health, and insurance sectors. However, any organization will most likely try to keep some patterns hidden once it shares its datasets with others. This article focuses on preserving the privacy of sensitive patterns when inducing decision trees. We propose a heuristic approach that can be used to hide a certain rule which can be inferred from the derivation of a binary decision tree. This hiding method is preferred over other heuristic solutions like output perturbation or cryptographic techniques—which limit the usability of the data—since the raw data itself is readily available for public use. This method can be used to hide decision tree rules with a minimum impact on all other rules derived.

【 授权许可】

Unknown