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

Privacy concerns have led to the development of privacy-preserving approachesfor learning models from sensitive data. Yet, in practice, even models learnedwith privacy guarantees can inadvertently memorize unique training examples orleak sensitive features. To identify such privacy violations, existing modelauditing techniques use finite adversaries defined as machine learning modelswith (a) access to some finite side information (e.g., a small auditingdataset), and (b) finite capacity (e.g., a fixed neural network architecture).Our work investigates the requirements under which an unsuccessful attempt toidentify privacy violations by a finite adversary implies that no strongeradversary can succeed at such a task. We do so via parameters that quantify thecapabilities of the finite adversary, including the size of the neural networkemployed by such an adversary and the amount of side information it has accessto as well as the regularity of the (perhaps privacy-guaranteeing) auditedmodel.

Theoretical Guarantees for Model Auditing with Finite Adversaries