Association rule hiding using enhanced elephant herding optimization algorithm

Abstract

Association rule hiding is an efficient solution that helps organizations to avoid the risk caused by sensitive knowledge leakage when sharing data in their collaborations. Cuckoo Optimization Algorithm (COA) sanitizes the transaction database but this method has limitation due to its slow convergence and exploitation capabilities. Hence in this paper, Enhanced Elephant Herding Optimization Algorithm for Association Rule Hiding (EEHOA4ARH) is proposed for association rule hiding. In EEHOA, two core functions such as clan updating operator and separating operator are used for association rule hiding that also realizes the fast convergence and exploitation capabilities. Moreover, the searching strategy in COA4ARH for the selection of best solution is highly time consuming. To reduce the time consumption for the selection of best solution, a Crowding Distance (CD) concept is combined with EEHOA4ARH. By continuously updating the best elephant and replacing the worst elephant in the population, EEHOA4ARH-CD sanitizes the transaction database effectively. Thus the proposed EEHOA4ARH achieves the less computation time, fast convergence and better exploitation capabilities by using crowding distance. The experimental results prove the effectiveness of the proposed EEHOA4ARH–CD method in terms of hiding failure, lost rule and execution time with 44.66 s.

Keywords

• Association rule hiding
• evolutionary algorithm
• cuckoo optimization algorithm
• enhanced elephant optimization algorithm
• crowding distance