REVIEW 1 cited by
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original 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).
Forward citations
Cited by 1 Pith paper
-
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.
Discussion (0). Continue with ORCID to comment.