High Performance Datafly based Anonymity Algorithm and Its L-Diversity

Data anonymity, as an effective privacy protection method, has been widely used in real applications. High performance data anonymity algorithm is especially attractive for those massive data applications. In this paper, the authors propose a novel and efficient Datafly based data anonymity Divide-Datafly algorithm and the experimental results show that the proposed algorithm is not only more efficient than Datafly and Incognito, but also information loss less than KACA. Moreover, in order to improve the security of anonymous data, L-Divide-Datafly is presented that it combines Divide-Datafly and efficient distance based clustering. Experimental results show that L-Divide-Datafly achieves great performance both in execution time and Information loss.