A secure multi-party computation scheme for matrix polynomials uses polynomial sharing and claims large worker savings, but its transpose procedure is wrong, breaking the arbitrary-polynomial result.
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning
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abstract
How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data and the model information-theoretically private, while allowing efficient parallelization of training across distributed workers. We characterize CodedPrivateML's privacy threshold and prove its convergence for logistic (and linear) regression. Furthermore, via extensive experiments on Amazon EC2, we demonstrate that CodedPrivateML provides significant speedup over cryptographic approaches based on multi-party computing (MPC).
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Secure Coded Multi-Party Computation for Massive Matrix Operations
A secure multi-party computation scheme for matrix polynomials uses polynomial sharing and claims large worker savings, but its transpose procedure is wrong, breaking the arbitrary-polynomial result.