Association rule hiding using grey wolf optimization algorithm

Abstract

Data mining of Privacy-preserving is another research field that way to ensure private data and ignore the spillage of data on the methodology of data mining. The objective of this examination is to secure individual data and to anticipate the introduction of this information during the data mining process. There are distinctive strategies in privacy data mining field. One of the strategies is association rule mining (ARM). The essential motivation behind ARM is to conceal sensitive association rules. PPARM is an imperative methodology in this field which hides the sensitive association rules. The algorithms of wide range have been made to hide delicate records. In this work, a new and powerful methodology has been shown for touchy data stowing away. GWO algorithm was proposed, in this algorithm data, contortion procedure was used to conceal the sensitive association rules. Fitness functions are used to accomplish the solution with the least indications. Likewise, the runtime has been decreased and provided better protection of data quality. The proposed technique’s efficiency was assessed by directing a few trials on various databases. The performance results of the proposed algorithms and two other existing algorithms on various database shows that the GWO Algorithm has higher efficiency contrasted to various algorithms.