基于拉普拉斯机制的集成分类隐私保护研究

Research on integrated Classification Privacy Protection based on Laplace mechanism

数字网络蓬勃发展,带来的信息泄漏已然造成数据危机。在隐私保护中,原始数据和模型都是隐私保护的对象,如何平衡其可用性和私密性一直是研究的重点。针对上述问题,文章提出了一种基于Laplace机制和XGboost特征筛选的集成分类隐私保护算法—Lap-XGIC。该算法利用XGboost筛选出对分类准确度最具影响的特征,在这些特征与集成分类系统中分别引入Laplace机制进行扰动,从而隐藏真实信息进行数据保护。实验表明,使用Laplace机制添加扰动噪声后集成分类系统仍然具有较高的分类准确性。

With the rapid development of digital network,information leakage has caused data crisis.In privacy protection,both raw data and model are the objects of privacy protection.How to balance its availability and privacy has always been the focus of research.To solve the above problems,this paper proposes an integrated classification privacy protection algorithm based on Laplace mechanism and XGboost feature filtering—Lap-XGIC.In this algorithm,XGboost is used to screen out the features that have the most influence on the classification accuracy,and Laplace mechanism is introduced into these features and the integrated classification system respectively for perturbation,so as to hide the real information for data protection.The experimental results show that the integrated classification system still has high classification accuracy after adding disturbance noise using Laplace mechanism.