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

This paper proposes a novel watchdog privatization scheme by generalizinglocal information privacy (LIP) to enhance data utility. To protect thesensitive features $S$ correlated with some useful data $X$, LIP restricts thelift, the ratio of the posterior belief to the prior on $S$ after and beforeaccessing $X$. For each $x$, both maximum and minimum lift over sensitivefeatures are measures of the privacy risk of publishing this symbol and shouldbe restricted for the privacy-preserving purpose. Previous works enforce thesame bound for both max-lift and min-lift. However, empirical observations showthat the min-lift is usually much smaller than the max-lift. In this work, wegeneralize the LIP definition to consider the unequal values of max and minlift, i.e., considering different bounds for max-lift and min-lift. This newdefinition is applied to the watchdog privacy mechanism. We demonstrate thatthe utility is enhanced under a given privacy constraint on local differentialprivacy. At the same time, the resulting max-lift is lower and, therefore,tightly restricts other privacy leakages, e.g., mutual information, maximalleakage, and $\alpha$-leakage.

Asymmetric Local Information Privacy and the Watchdog Mechanism