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

We consider the problem of publishing location datasets, in particular 2Dspatial pointsets, in a differentially private manner. Many existing mechanismsfocus on frequency counts of the points in some a priori partition of thedomain that is difficult to determine. We propose an approach that adds noisedirectly to the point, or to a group of neighboring points. Our approach isbased on the observation that, the sensitivity of sorting, as a function onsets of real numbers, can be bounded. Together with isotonic regression, thedataset can be accurately reconstructed. To extend the mechanism to higherdimension, we employ locality preserving function to map the dataset to abounded interval. Although there are fundamental limits on the performance oflocality preserving functions, fortunately, our problem only requires distancepreservation in the "easier" direction, and the well-known Hilbertspace-filling curve suffices to provide high accuracy. The publishing processis simple from the publisher's point of view: the publisher just needs to mapthe data, sort them, group them, add Laplace noise and publish the dataset. Theonly parameter to determine is the group size which can be chosen based onpredicted generalization errors. Empirical study shows that the publisheddataset can also exploited to answer other queries, for example, range queryand median query, accurately.

Publishing Location Dataset Differential Privately with Isotonic Regression