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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We introduce a new concept, data irrecoverability, and show that thewell-studied concept of data privacy is sufficient but not necessary for datairrecoverability. We show that there are several regularized loss minimizationproblems that can use perturbed data with theoretical guarantees ofgeneralization, i.e., loss consistency. Our results quantitatively connect theconvergence rates of the learning problems to the impossibility for anyadversary for recovering the original data from perturbed observations. Inaddition, we show several examples where the convergence rates with perturbeddata only increase the convergence rates with original data within a constantfactor related to the amount of perturbation, i.e., noise.

Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability