基于抽样路径的K-匿名隐私保护算法

K-anonymous privacy protection algorithm based on the sampling path

K-匿名是信息隐私保护的一种常用技术,而使用K-匿名技术不可避免会造成发布数据的信息损失,因此,如何提高K-匿名化后数据集的可用性一直以来都是K-匿名隐私保护的研究重点。对此提出了一种基于抽样路径的局域泛化算法——SPOLG算法。该算法基于泛化格寻找信息损失较小的泛化路径,为减少寻径时间,引入等概率抽样的思想,选用等概率抽样中的系统抽样方法进行取样,利用样本代替数据集在泛化格上寻找目标泛化路径,最后在该路径上对数据集进行泛化。同时,本算法使用局域泛化技术,能够降低信息损失量,提高发布数据集的可用性。实验结果证明,本算法匿名化的数据集信息损失度低,数据可用性高。

K-anonymity is a common technique of information privacy protection. But K-anonymity must cause the information loss and how to improve the usability of anonymizing tables is the emphasis of K-anonymity. To solve the problem, this paper puts forward a kind of anonymous privacy protection algorithm based on generalized path --SPOLG algorithm. It introduces sampling with equal probabilities to find the generalized path whose information loss is little and raise the efficiency of data processing. Experimental results show that the algorithm could satisfy the anonymous requirement, at the same time, it is more efficient than Incognito algorithm to reduce the information loss of data released.