atlas

Research map / Federated learning and privacy / Differential privacy

Fully-Adaptive Composition in Differential Privacy

ICML 2023 · Federated learning and privacy › Differential privacy · approach: Private data analysis frameworks · 5 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Measuring accumulated privacy loss in terms of intrinsic time determined by observed privacy parameters (rather than number of algorithms)fully adaptive composition of differentially private algorithms · theoretical analysis (advanced composition with adaptively chosen privacy parameters)rate of advanced compositionHelped
Reducing arbitrary conditionally differentially private algorithms to post-processings of conditional randomized responseadaptive composition for conditionally differentially private algorithms · theoretical analysisrequired assumption for composition resultsHelped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy