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

Computing an AUC as a performance measure to compare the quality of differentmachine learning models is one of the final steps of many research projects.Many of these methods are trained on privacy-sensitive data and there areseveral different approaches like $\epsilon$-differential privacy, federatedmachine learning and cryptography if the datasets cannot be shared or usedjointly at one place for training and/or testing. In this setting, it can alsobe a problem to compute the global AUC, since the labels might also containprivacy-sensitive information. There have been approaches based on$\epsilon$-differential privacy to address this problem, but to the best of ourknowledge, no exact privacy preserving solution has been introduced. In thispaper, we propose an MPC-based solution, called ppAURORA, with private mergingof individually sorted lists from multiple sources to compute the exact AUC asone could obtain on the pooled original test samples. With ppAURORA, thecomputation of the exact area under precision-recall and receiver operatingcharacteristic curves is possible even when ties between prediction confidencevalues exist. We use ppAURORA to evaluate two different models predicting acutemyeloid leukemia therapy response and heart disease, respectively. We alsoassess its scalability via synthetic data experiments. All these experimentsshow that we efficiently and privately compute the exact same AUC with bothevaluation metrics as one can obtain on the pooled test samples in plaintextaccording to the semi-honest adversary setting.

ppAURORA: Privacy Preserving Area Under Receiver Operating Characteristic and Precision-Recall Curves