English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

How to train a machine learning model while keeping the data private andsecure? We present CodedPrivateML, a fast and scalable approach to thiscritical problem. CodedPrivateML keeps both the data and the modelinformation-theoretically private, while allowing efficient parallelization oftraining across distributed workers. We characterize CodedPrivateML's privacythreshold and prove its convergence for logistic (and linear) regression.Furthermore, via extensive experiments on Amazon EC2, we demonstrate thatCodedPrivateML provides significant speedup over cryptographic approaches basedon multi-party computing (MPC).

CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning