  Nowadays, the utilization of the ever expanding amount of data has made a
huge impact on web technologies while also causing various types of security
concerns. On one hand, potential gains are highly anticipated if different
organizations could somehow collaboratively share their data for technological
improvements. On the other hand, data security concerns may arise for both data
holders and data providers due to commercial or sociological concerns. To make
a balance between technical improvements and security limitations, we implement
secure and scalable protocols for multiple data holders to train linear
regression and logistic regression models. We build our protocols based on the
secret sharing scheme, which is scalable and efficient in applications.
Moreover, our proposed paradigm can be generalized to any secure multiparty
training scenarios where only matrix summation and matrix multiplications are
used. We demonstrate our approach by experiments which shows the scalability
and efficiency of our proposed protocols, and finally present its real-world
applications.
