Hybrid k-anonymity approach based on TDS and BUG under the environment of big data cloud - Xiaofeng FAN; Feng YAN; Yang LIU (Dianxin kexue)
As the issue of low efficiency and poor scalability in general sub-tree anonymous method of treating big data,a bottom-up generalization(BUG) method with scalability was proposed,and on this basis,combined with the existing top-down specialization(TDS),a hybrid approach was formed.In the proposed method,k-anonymity was being as a privacy model,the compositions of TDS and BUG were developed with mapping simplification,and higher scalability through powerful cloud computing capabilities were achieved.The proposed mapping simplification BUG could insert a new candidate after several cycles of generalization,and would not affect information loss of another generalization.Given the complexity of the relationship between workload balancing point K and anonymous parameter k,mapping simplifications of BUG and TDS were combined to form a hybrid approach.Experimental results demonstrate the effectiveness of the proposed method and compared with TDS and BUG,the efficiency and scalability of hybrid method are greatly improved.