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

Classification of personal text messages has many useful applications insurveillance, e-commerce, and mental health care, to name a few. Givingapplications access to personal texts can easily lead to (un)intentionalprivacy violations. We propose the first privacy-preserving solution for textclassification that is provably secure. Our method, which is based on SecureMultiparty Computation (SMC), encompasses both feature extraction from texts,and subsequent classification with logistic regression and tree ensembles. Weprove that when using our secure text classification method, the applicationdoes not learn anything about the text, and the author of the text does notlearn anything about the text classification model used by the applicationbeyond what is given by the classification result itself. We perform end-to-endexperiments with an application for detecting hate speech against women andimmigrants, demonstrating excellent runtime results without loss of accuracy.

Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech Detection