论文标题
具有完全同态加密的保密性混乱极端学习机
Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption
论文作者
论文摘要
机器学习和深度学习模型需要大量的培训过程数据，在某些情况下，可能会有一些敏感数据，例如涉及的客户信息，组织可能会犹豫要外包用于模型构建。一些隐私保护技术，例如差异隐私，同态加密和安全的多方计算，可以与不同的机器学习和深度学习算法集成，以为数据以及模型提供安全性。在本文中，我们使用完全同型加密提出了一种混乱的极端学习机及其加密形式，其中使用逻辑图而不是统一分布生成权重和偏见。我们提出的方法在大多数数据集中都可以更好地或类似于传统的极限学习机器。
The Machine Learning and Deep Learning Models require a lot of data for the training process, and in some scenarios, there might be some sensitive data, such as customer information involved, which the organizations might be hesitant to outsource for model building. Some of the privacy-preserving techniques such as Differential Privacy, Homomorphic Encryption, and Secure Multi-Party Computation can be integrated with different Machine Learning and Deep Learning algorithms to provide security to the data as well as the model. In this paper, we propose a Chaotic Extreme Learning Machine and its encrypted form using Fully Homomorphic Encryption where the weights and biases are generated using a logistic map instead of uniform distribution. Our proposed method has performed either better or similar to the Traditional Extreme Learning Machine on most of the datasets.
