Abstract
Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper.
Highlights
We developed a privacy protected machine learning algorithm, named Paillier Federated Multi-Layer Perceptron (PFMLP), based on homomorphic encryption
There is some work focusing on federated transfer learning, such as the framework designed in [36], that can be flexibly applied to various secure multi-party machine learning
The experiments on the MNIST dataset show that the model trained by PFMLP can reach an accuracy rate of 0.9252 on the testing set, while the model trained by MLP using all of the data of training set can reach an accuracy rate of 0.9245, just 0.007 lower than that of the PFMLP algorithm
Summary
In the big data era, data privacy has become one of the most significant issues. far, there exist plenty of security strategies and encryption algorithms which try to ensure that sensitive data would not be compromised. We developed a privacy protected machine learning algorithm, named PFMLP, based on homomorphic encryption. The multi-party privacy protected machine learning based on homomorphic encryption proposed in the paper has a wide range of scenarios in practical applications. The main contributions of the work are as follows: It provides a multi-party privacy protected machine learning framework that combines homomorphic encryption and federated learning to achieve the protection of data and model security during model training. The proposed framework can maintain the privacy data security when multiple parties learn together. It verifies that the model trained by our proposed algorithm has a similar accuracy rate as the model trained by traditional methods.
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