KAB: A new k-anonymity approach based on black hole algorithm

K-anonymity is the most widely used approach to privacy preserving microdata which mainly based on generalization. Although generalization-based k-anonymity approaches can achieve protection objective, they suffer from information loss. Clustering-based have been successfully adapted for k-anonymization as enhance data quality, however, computational complexity of finding an optimal solution has shown NP-hard. Nature-inspired optimization algorithms are effective in solutions complex problems. We propose, this paper, a novel algorithm simple nature-inspired metaheuristic called Black Hole Algorithm (BHA), address such limitations. Experiments real set show that utility improved by our compared k-anonymity, BHA-based and clustering-based approaches.