2 research outputs found
Data set operations to hide decision tree rules
This paper focuses on preserving the privacy of sensitive patterns when
inducing decision trees. We adopt 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 - which restrict the usability of the data - since the
raw data itself is readily available for public use. We show some key lemmas
which are related to the hiding process and we also demonstrate the methodology
with an example and an indicative experiment using a prototype hiding tool.Comment: 7 pages, 4 figures and 2 tables. ECAI 201
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.Comment: 10 pages, 5 figures. arXiv admin note: substantial text overlap with
arXiv:1706.0573