Matrix Commitment-Based Ownership Verification for Distributed Machine Learning

Summary

This paper proposes MAMMON, a Matrix Commitment-based ownership verification scheme for Distributed Machine Learning (DML). It uses a multi-linear tree structure to cut proof update costs, avoiding the heavier SNARK-based approaches and their arithmetic circuit computations. Experiments reportedly show better preservation of computational integrity in DML training than existing baselines.