A Fuzzy-Assisted Mathematically Modified Cat Swarm Optimization Approach for Effective Sensitive Association Rule Hiding in Data Mining

Abstract

Protecting and storing the confidential data imposes critical distress of privacy. Preserving the confidential data privacy is attained using Privacy Preserving Data Mining (PPDM). One of the significant problems in Privacy Preservation Data Mining is Association Rule Hiding (ARH) and it is utilised in hiding sensitive association rules. Every ARH algorithms intended to alter the original database such that no confidential association rules are derived from the transactional database. ARH approaches are extensively used in data mining to spot the association among the itemset. Most of the business organizations reveal certain information to the third party for the common benefit of identifying the needed knowledge for promoting the business schemes and decision making. Database may possess the private information where a business organization does not want to share that information to the third party. The problem of privacy plays a significant role when varied organizations share the data for the benefit by compromising the privacy of the individual person. Before revealing the information, confidential data in the database must be masked by PPDM approach, which is helpful in advance to security of the database. The proposed algorithmUnified Transaction Dimensionality Reduction Framework (UTDRF) uses Fuzzy Cat Swarm Optimization algorithm (FCSO) to hide the sensitive items in the transactional database. The proposed method is compared with the existing state-of-art algorithms. From the test it has been justified that the FCSO algorithm produced better results than existing algorithms.