Fishers Filtered Gravitational Rule Selection and Weighted Rank Based Genetic Algorithm for Association Rule Hiding to Preserve Privacy in Transactional Database - Gayathiri, P; Poorna, B (International Journal of Computer Sciences and Engineering, 2021)
Several methods had been investigated in the literature for rule hiding involving sensitive items. Some methods use co-operative models for mining functional association rules and some use distortion-based rule hiding technique. The present paper focuses on fast mining of rules using rank-based sensitive rule hiding framework called, Fisher’s Filtered Gravitational Search and Rank-based Gene (FFGS-RG) for hiding sensitive association rules. To start with Fisher’s Filtered is applied to filter the association rule and speeding up the mining process among the generated rule with Gravitational Search technique to select the sensitive rules from the transactional database. Once the sensitive rules are selected, the gene property of hidden and exposed items is mapped to the vector data item of sensitive rules for minimum distortion based on weighted ranking. The new gene data item population is generated using genetic algorithm operations to minimize the distortion via ranking. With distorted minimized offspring gene data item population, new sensitive rules are generated using Fisher’s test that speeds up the rule selection process and provided to the transactional users. The distorted minimized offspring generated new rules are obtained then tested for side effects. This process is continued till the final sensitive rule hiding has minimal distortion on the gene populated data item rules and higher data item utility to the transactional users using weighted rank. A benchmark dataset is used to evaluate the FFGS-RG framework and the results show more efficient in improving the rule hiding accuracy with minimal rule selection time and also optimizing the sensitive rules hiding process.