摘要
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.
出处
《电信科学》
北大核心
2016年第7期90-96,共7页
Telecommunications Science
关键词
云计算
子树匿名化
大数据
泛化
特化
映射化简
cloud computing, sub-tree anonymous, big data, generalization, specialization, mapping simplification