Search Results for author:
Found 2 papers, 1 papers with code
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase.
RankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets
This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense datasets.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
