Hybrid Evolutionary Algorithm for Preserving Privacy of Sensitive Data in Quantitative Databases

Abstract

<span style="font-size:9.0pt;font-family:" times="" new="" roman";="" mso-fareast-font-family:calibri;mso-bidi-font-family:mangal;mso-ansi-language:="" en-in;mso-fareast-language:en-us;mso-bidi-language:hi"="">Association rule mining technique has been widely used in various applications. However, the abuse of this technique may lead to the discovery of sensitive information. Researchers in recent times have made effort for hiding sensitive association rules. But most of the techniques proposed are generally applied in binary dataset. It suffers from side effects of lost and ghost rule. Most business, medical and scientific domains has quantitative value for its attributes. Limited research is available for hiding sensitive information in quantitative data. The aim of privacy preserving quantitative association rule mining is to i. Prevent the discovery of sensitive information. ii. Not to compromise the access and the use of non sensitive data. iii. Be utilizable on large amounts of data iv. Not to have an exponential computational complexity. In this paper, a hybrid evolutionary algorithm is proposed for effectively hiding the sensitive quantitative association rules and for improving the utility of the database. The performance of the proposed system is compared with existing algorithm by measuring number of lost rules, number of ghost rules and number of modifications to the original data.</span>