A Noise Addition Scheme in Decision Tree for Privacy Preserving Data Mining

TL;DR

This paper introduces a noise addition scheme for decision trees that enhances privacy preservation in data mining by obfuscating numeric attributes, enabling secure analysis without revealing original data.

Contribution

The paper proposes a novel noise addition method applied after decision tree exploration to protect data privacy during collaborative data mining.

Findings

Decision trees on original and obfuscated data are similar

The method effectively preserves data privacy during mining

Obfuscated data maintains utility for decision tree analysis

Abstract

Data mining deals with automatic extraction of previously unknown patterns from large amounts of data. Organizations all over the world handle large amounts of data and are dependent on mining gigantic data sets for expansion of their enterprises. These data sets typically contain sensitive individual information, which consequently get exposed to the other parties. Though we cannot deny the benefits of knowledge discovery that comes through data mining, we should also ensure that data privacy is maintained in the event of data mining. Privacy preserving data mining is a specialized activity in which the data privacy is ensured during data mining. Data privacy is as important as the extracted knowledge and efforts that guarantee data privacy during data mining are encouraged. In this paper we propose a strategy that protects the data privacy during decision tree analysis of data mining process. We propose to add specific noise to the numeric attributes after exploring the decision tree of the original data. The obfuscated data then is presented to the second party for decision tree analysis. The decision tree obtained on the original data and the obfuscated data are similar but by using our method the data proper is not revealed to the second party during the mining process and hence the privacy will be preserved.