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

Many organizations wish to collaboratively train machine learning models ontheir combined datasets for a common benefit (e.g., better medical research, orfraud detection). However, they often cannot share their plaintext datasets dueto privacy concerns and/or business competition. In this paper, we design andbuild Helen, a system that allows multiple parties to train a linear modelwithout revealing their data, a setting we call coopetitive learning. Comparedto prior secure training systems, Helen protects against a much strongeradversary who is malicious and can compromise m-1 out of m parties. Ourevaluation shows that Helen can achieve up to five orders of magnitude ofperformance improvement when compared to training using an existingstate-of-the-art secure multi-party computation framework.

Helen: Maliciously Secure Coopetitive Learning for Linear Models