  With the increasing demands for privacy protection, many privacy-preserving
machine learning systems were proposed in recent years. However, most of them
cannot be put into production due to their slow training and inference speed
caused by the heavy cost of homomorphic encryption and secure multiparty
computation(MPC) methods. To circumvent this, I proposed a privacy definition
which is suitable for large amount of data in machine learning tasks. Based on
that, I showed that random transformations like linear transformation and
random permutation can well protect privacy. Merging random transformations and
arithmetic sharing together, I designed a framework for private machine
learning with high efficiency and low computation cost.
