An Efficient and Privacy preserving Computation Framework for Tibetan medicine

With the continuous development of Tibetan medicine, using machine learning technology to enhance the value of Tibetan medical data has become very important. However, the concerns of Tibetan medical institutions about data leakage have hindered the sharing of Tibetan medical data. Therefore, in this paper, we propose a privacy preserving computation framework based on dual servers. Our framework can securely store Tibetan medical data on cloud servers. The secure computation (such as machine learning training or machine learning prediction) is performed by cloud servers without compromising data. On the premise of ensuring data security, we combine the multi-key homomorphic encryption and secret sharing to design some secure building blocks. Through the security analysis and performance evaluation, our proposed scheme is efficient and practical.