Hiding decision tree rules by data set operations

• Published in: 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA) pp. 1 - 6
• Main Authors: Kalles, Dimitris, Verykios, Vassilios S., Papagelis, Athanasios
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2015
• Subjects: Classification algorithms; Cryptography; Data privacy; decision tree; Decision trees; Entropy; information gain; rule hiding; Yttrium
• DOI: 10.1109/IISA.2015.7387954

This paper focuses on preserving the privacy of sensitive patterns in the context of inducing decision trees. The subject at hand is approached through 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 - that restrict the usability of the data in different ways - since the raw data itself is readily available for public use. This methodology is based upon the unique characteristics of the induction of binary decision trees with binary-valued symbolic attributes and binary classes.