Vikram Garg

A Survey of Association Rule Hiding Algorithms

The significant development in field of data collection and data storage technologies have provided transactional data to grow in data warehouses that reside in companies and public sector organizations. As the data is growing day by day, there has to be certain mechanism that could analyze such large volume of data. Data mining is a way of extracting the hidden predictive information from those data warehouses without revealing their sensitive information. Privacy preserving data mining (PPDM) is the recent research area that deals with the problem of hiding the sensitive information while analyzing data. Association Rule Hiding is one of the techniques of PPDM to hide association rules generated by Association Rule Generation Algorithms. In this paper we will provide a comparative theoretical analysis of Algorithms that have been developed for Association Rule Hiding.

A Hybrid Algorithm for Association Rule Hiding using Representative Rule

In the recent years, data mining has emerged as a very popular tool for extracting hidden knowledge from collection of large amount of data. One of the major challenges of data mining is to find the hidden knowledge in the data while the sensitive information is not revealed. Many strategies have been proposed to hide the information containing sensitive data. Privacy preserving data mining is an answer to such challenge. Association rule hiding is one of the PPDM techniques to protect the sensitive association rule generated by Association rule mining (ARM). In this paper, the data distortion technique for hiding the sensitive information is used. The proposed approach uses the concept of Representative Rule (RR) which is used to prune the number of association rule. The proposed algorithm hides the more number of rules while making the fewer database scans.