基于不同敏感度的改进K-匿名隐私保护算法

Improved K-anonymity privacy protection algorithm based on different sensitivities

针对机器学习的发展需要大量兼顾数据安全性和可用性的真实数据集的问题,提出一种基于随机森林(RF)的K-匿名隐私保护算法——RFK-匿名隐私保护。首先,使用RF算法预测出每种属性值的敏感程度;然后,使用k-means聚类算法将属性值根据不同敏感程度进行聚类,再使用K-匿名算法根据属性值的敏感程度集群对数据进行不同程度的隐匿;最后,由用户自主地选择需要哪种隐匿程度的数据表。实验结果表明,在Adult数据集中,与K-匿名算法处理过的数据相比,RFK-匿名隐私保护算法处理过的数据在阈值分别为3、4时的准确率分别提高了0.5、1.6个百分点;与(p,α,k)-匿名算法处理过的数据相比,RFK-匿名隐私保护算法处理过的数据在阈值分别为4、5时的准确率分别提高了0.4、1.9个百分点。RFK-匿名隐私保护算法在保护数据的隐私安全的基础上能有效提高数据的可用性,更适合应用于机器学习中的分类预测。

To address the problem that the development of machine learning requires a large number of real datasets with both data security and availability,an improved K-anonymity privacy protection algorithm based on Random Forest(RF)was proposed,namely RFK-anonymity privacy protection.Firstly,the sensitivity of each attribute value was predicted by RF algorithm.Secondly,the attribute values were clustered according to different sensitivities by using the k-means clustering algorithm,and the data was hidden to different degrees by using the K-anonymity algorithm according to the sensitivity clusters of attribution.Finally,data tables with different hiding degrees were selected by different users according to their needs.Experimental results show that in Adult datasets,compared with the data processed by K-anonymity algorithm,the accuracies of the data processed by the RFK-anonymity privacy protection algorithm are increased by 0.5 and 1.6 percentage points at thresholds of 3 and 4,respectively;compared with the data processed by(p,α,k)-anonymity algorithm,the accuracies of the data processed by the proposed algorithm are improved by 0.4 and 1.9 percentage points at thresholds of 4 and 5.It can be seen that RFK-anonymity privacy protection algorithm can effectively improve the availability of data on the basis of protecting the privacy and security of data,and it is more suitable for classification and prediction in machine learning.