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

To meet the standard of differential privacy, noise is usually added into theoriginal data, which inevitably deteriorates the predicting performance ofsubsequent learning algorithms. In this paper, motivated by the success ofimproving predicting performance by ensemble learning, we propose to enhanceprivacy-preserving logistic regression by stacking. We show that this can bedone either by sample-based or feature-based partitioning. However, we provethat when privacy-budgets are the same, feature-based partitioning requiresfewer samples than sample-based one, and thus likely has better empiricalperformance. As transfer learning is difficult to be integrated with adifferential privacy guarantee, we further combine the proposed method withhypothesis transfer learning to address the problem of learning acrossdifferent organizations. Finally, we not only demonstrate the effectiveness ofour method on two benchmark data sets, i.e., MNIST and NEWS20, but also applyit into a real application of cross-organizational diabetes prediction fromRUIJIN data set, where privacy is of significant concern.

Differential Private Stack Generalization with an Application to Diabetes Prediction