论文标题
具有完全同态加密的隐私保护小波神经网络
Privacy-Preserving Wavelet Neural Network with Fully Homomorphic Encryption
论文作者
论文摘要
隐私机器学习（PPML）的主要目的是保护隐私并为建筑机器学习模型中使用的数据提供安全性。 PPML中有各种技术，例如安全的多方计算，差异隐私和同形加密（HE）。这些技术与各种机器学习模型甚至深度学习网络相结合，以保护数据隐私以及用户的身份。在本文中，我们提出了一个完全同型加密的小波神经网络，以保护隐私，同时不妥协模型的效率。我们在从金融和医疗保健领域获取的七个数据集上测试了提出方法的有效性。结果表明，我们提出的模型的性能与未加密的模型相似。
The main aim of Privacy-Preserving Machine Learning (PPML) is to protect the privacy and provide security to the data used in building Machine Learning models. There are various techniques in PPML such as Secure Multi-Party Computation, Differential Privacy, and Homomorphic Encryption (HE). The techniques are combined with various Machine Learning models and even Deep Learning Networks to protect the data privacy as well as the identity of the user. In this paper, we propose a fully homomorphic encrypted wavelet neural network to protect privacy and at the same time not compromise on the efficiency of the model. We tested the effectiveness of the proposed method on seven datasets taken from the finance and healthcare domains. The results show that our proposed model performs similarly to the unencrypted model.
