Secret Sharing based Secure Regressions with Applications
This work addresses data security and collaboration challenges for organizations handling sensitive data, though it is incremental as it applies existing secret sharing techniques to regression tasks.
The paper tackles the problem of enabling multiple organizations to collaboratively train linear and logistic regression models while addressing data security concerns, implementing scalable protocols based on secret sharing that demonstrate efficiency in experiments.
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.