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 present a method for producing unbiased parameter estimates and validconfidence intervals under the constraints of differential privacy, a formalframework for limiting individual information leakage from sensitive data.Prior work in this area is limited in that it is tailored to calculatingconfidence intervals for specific statistical procedures, such as meanestimation or simple linear regression. While other recent work can produceconfidence intervals for more general sets of procedures, they either yieldonly approximately unbiased estimates, are designed for one-dimensionaloutputs, or assume significant user knowledge about the data-generatingdistribution. Our method induces distributions of mean and covariance estimatesvia the bag of little bootstraps (BLB) and uses them to privately estimate theparameters' sampling distribution via a generalized version of the CoinPressestimation algorithm. If the user can bound the parameters of the BLB-inducedparameters and provide heavier-tailed families, the algorithm produces unbiasedparameter estimates and valid confidence intervals which hold with arbitrarilyhigh probability. These results hold in high dimensions and for any estimationprocedure which behaves nicely under the bootstrap.

Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy