Cao Xiao-Kai, Wang Chang-Dong, Lai Jian-Huang, Huang Qiong, Chen C L Philip
IEEE Trans Cybern. 2023 Oct;53(10):6636-6648. doi: 10.1109/TCYB.2023.3235496. Epub 2023 Sep 15.
Multiparty learning is an indispensable technique to improve the learning performance via integrating data from multiple parties. Unfortunately, directly integrating multiparty data could not meet the privacy-preserving requirements, which then induces the development of privacy-preserving machine learning (PPML), a key research task in multiparty learning. Despite this, the existing PPML methods generally cannot simultaneously meet multiple requirements, such as security, accuracy, efficiency, and application scope. To deal with the aforementioned problems, in this article, we present a new PPML method based on the secure multiparty interactive protocol, namely, the multiparty secure broad learning system (MSBLS) and derive its security analysis. To be specific, the proposed method employs the interactive protocol and random mapping to generate the mapped features of data, and then uses efficient broad learning to train the neural network classifier. To the best of our knowledge, this is the first attempt for privacy computing method that jointly combines secure multiparty computing and neural network. Theoretically, this method can ensure that the accuracy of the model will not be reduced due to encryption, and the calculation speed is very fast. Three classical datasets are adopted to verify our conclusion.
多方学习是一种通过整合多方数据来提高学习性能的不可或缺的技术。不幸的是，直接整合多方数据无法满足隐私保护要求，这进而催生了隐私保护机器学习（PPML）的发展，这是多方学习中的一项关键研究任务。尽管如此，现有的PPML方法通常无法同时满足多个要求，如安全性、准确性、效率和应用范围。为了解决上述问题，在本文中，我们提出了一种基于安全多方交互协议的新PPML方法，即多方安全广义学习系统（MSBLS），并进行了安全性分析。具体而言，所提出的方法采用交互协议和随机映射来生成数据的映射特征，然后使用高效的广义学习来训练神经网络分类器。据我们所知，这是首次将安全多方计算和神经网络联合起来的隐私计算方法尝试。从理论上讲，该方法可以确保模型的准确性不会因加密而降低，并且计算速度非常快。采用三个经典数据集来验证我们的结论。
