MACHINE LEARNING WITH DIFFERENTLY MASKED DATA IN SECURE MULTI-PARTY COMPUTING

In a secure multi-party computation (sMPC) system, a super mask is constructed using a set of masks corresponding to a set of data contributors. Each data contributor uses a corresponding different mask to obfuscate the data of the data contributor. a first scaled masked data is formed by applying a first scale factor to first masked data of the first data contributor, the scale factor being computed specifically for the first data contributor from the super mask. A union is constructed of all scaled masked data from all data contributors, including the first scaled masked data. A machine learning (ML) model is trained using the union as training data, where the union continues to keep obfuscated the differently masked data from the different data contributors. The training produces a trained ML model usable in the sMPC with the set of data contributors.