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

The collection and sharing of genomic data are becoming increasinglycommonplace in research, clinical, and direct-to-consumer settings. Thecomputational protocols typically adopted to protect individual privacy includesharing summary statistics, such as allele frequencies, or limiting queryresponses to the presence/absence of alleles of interest using web-servicescalled Beacons. However, even such limited releases are susceptible tolikelihood-ratio-based membership-inference attacks. Several approaches havebeen proposed to preserve privacy, which either suppress a subset of genomicvariants or modify query responses for specific variants (e.g., adding noise,as in differential privacy). However, many of these approaches result in asignificant utility loss, either suppressing many variants or adding asubstantial amount of noise. In this paper, we introduce optimization-basedapproaches to explicitly trade off the utility of summary data or Beaconresponses and privacy with respect to membership-inference attacks based onlikelihood-ratios, combining variant suppression and modification. We considertwo attack models. In the first, an attacker applies a likelihood-ratio test tomake membership-inference claims. In the second model, an attacker uses athreshold that accounts for the effect of the data release on the separation inscores between individuals in the dataset and those who are not. We furtherintroduce highly scalable approaches for approximately solving theprivacy-utility tradeoff problem when information is either in the form ofsummary statistics or presence/absence queries. Finally, we show that theproposed approaches outperform the state of the art in both utility and privacythrough an extensive evaluation with public datasets.

Enabling Trade-offs in Privacy and Utility in Genomic Data Beacons and Summary Statistics