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

Differential Privacy (DP) provides tight upper bounds on the capabilities ofoptimal adversaries, but such adversaries are rarely encountered in practice.Under the hypothesis testing/membership inference interpretation of DP, weexamine the Gaussian mechanism and relax the usual assumption of aNeyman-Pearson-Optimal (NPO) adversary to a Generalized Likelihood Test (GLRT)adversary. This mild relaxation leads to improved privacy guarantees, which weexpress in the spirit of Gaussian DP and $(\varepsilon, \delta)$-DP, includingcomposition and sub-sampling results. We evaluate our results numerically andfind them to match the theoretical upper bounds.

Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens