arXiv (All)
open access
Diffusion Sensitivity Geometry and Strong Duality for Optimal Privacy-Utility Tradeoffs
Existing approaches to differential privacy in diffusion models rely on loose sensitivity bounds and isotropic Gaussian noise, leading to unnecessary estimation error. We develop a framework for the optimal privacy-utility tradeoff of ideal diffusion denoisers, replacing conservative bounds with exa
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Diffusion Sensitivity Geometry and Strong Duality for Optimal Privacy-Utility Tradeoffs#1406396
- Privacy Amplification Without Independence: How Far Negative Dependence Carries the Guarantees of Poisson Subsampling#822359
- Privacy Accounting for Empirical Denoisers#1403506
- Privacy Accounting for Empirical Denoisers#1394155
- The Privacy Subsidy: Kyle's $λ$ under Noise-Perturbed Order-Flow Observation#196413
Record · ID 1413232
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive