Privacy preserving quantitative association rule mining using convex optimization technique

Abstract

Privacy preserving data mining (PPDM) has been a new research area in the past two decades. The aim of PPDM algorithms is to modify data in the dataset so that sensitive data and confidential knowledge, even after mining data be kept confidential. Association rule hiding is one of the techniques of PPDM to avoid extracting some rules that are recognized as sensitive rules and should be extracted and placed in the public domain. Most of the work has been done in the area of privacy preserving data mining are limited to binary data, however many real world datasets include quantitative data too. In this paper a new methods is proposed to hide sensitive quantitative association rules which is based on convex optimization technique. In this method, fuzzy association rule hiding is formulated as a convex optimization problem and experiments have been carried out on the real dataset. The results of experiments indicate that the proposed method outperformed exiting methods at this field in the term of percentage of missing rules and changes made in the dataset.