论文标题
保留多方建模的PCA的隐私
Privacy Preserving PCA for Multiparty Modeling
论文作者
论文摘要
在本文中，我们提出了一个通用多方建模范式，其中保留了用于水平分区数据的主体组件分析（PPPCA）。 PPPCA可以在当地保存明文数据的前提下完成PCA的多方合作执行。我们还建议使用两种技术，即同型加密和秘密共享实施。 PPPCA的输出可以直接发送给数据消费者以构建任何机器学习模型。我们在三个UCI基准数据集和一个现实世界欺诈检测数据集上进行实验。结果表明，基于PPPCA构建的模型的准确性与基于集中式明文数据构建的PCA模型相同。
In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data. PPPCA can accomplish multiparty cooperative execution of PCA under the premise of keeping plaintext data locally. We also propose implementations using two techniques, i.e., homomorphic encryption and secret sharing. The output of PPPCA can be sent directly to data consumer to build any machine learning models. We conduct experiments on three UCI benchmark datasets and a real-world fraud detection dataset. Results show that the accuracy of the model built upon PPPCA is the same as the model with PCA that is built based on centralized plaintext data.
