Homo-ELM: fully homomorphic extreme learning machine

Extreme learning machine (ELM) as a machine learning method has been successfully applied to many classification problems. However, when applying ELM to classification tasks on the encrypted data in cloud, the classification performance is extremely low. Due to the data encryption, ELM is hard to extract informative features from the encrypted data for correct classification. Moreover, the trained neural network is un-protected on the cloud environments, that makes cloud service highly risky to the attackers. In this paper, we propose a novel fully homomorphic ELM (Homo-ELM), which makes cloud searching tasks under a fully protected environment without compromising the privacy of users. To demonstrate the effectiveness of our approach, we conduct a comprehensive experiment on both cloud and local environments. The experiment results show that Homo-ELM can achieve high accuracy on the local environments as well as cloud environments than other machine learning methods.