大数据云环境下TDS和BUG混合k-匿名化方法

Hybrid k-anonymity approach based on TDS and BUG under the environment of big data cloud

针对一般子树匿名化方法处理大数据效率低和伸缩性较差的问题,提出了一种可伸缩的自下向上的泛化(BUG)方法,并在此基础上,结合已有的自上向下的特化(TDS),形成一种混合方法。在提出的方法中,k-匿名作为隐私模型,TDS和BUG都是基于映射化简开发组成,并通过云的强大计算能力来获得较高的伸缩性。提出的映射化简BUG只需在几次泛化循环之后就可插入一个新的泛化候选,不会影响另一个泛化的信息损失。考虑到工作负载平衡点K与匿名参数k的复杂关系,将映射化简的BUG和TDS结合形成混合方法。实验结果验证了本文方法的有效性,与TDS和BUG相比,混合方法的效率和可伸缩性大为提高。

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.