mlsanityData to 7 Oct 2026

Fully Adaptive Composition for Gaussian Differential Privacy

arXiv 2210.17520 ↗First posted 31 Oct 2022cs.CR cs.LGcited by 4

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Abstract (arXiv)

We show that Gaussian Differential Privacy, a variant of differential privacy tailored to the analysis of Gaussian noise addition, composes gracefully even in the presence of a fully adaptive analyst. Such an analyst selects mechanisms (to be run on a sensitive data set) and their privacy budgets adaptively, that is, based on the answers from other mechanisms run previously on the same data set. In the language of Rogers, Roth, Ullman and Vadhan, this gives a filter for GDP with the same parameters as for nonadaptive composition.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 2 of its 3 references that are in the map. First posted on arXiv 31 Oct 2022.

Builds on

2 of 3 references.

References · 2

  1. 2016Privacy Odometers and Filters: Pay-as-you-Go Compositioncited by 32
  2. 2022Individual Privacy Accounting with Gaussian Differential Privacycited by 19

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Privatecited by 2
  2. 2025Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusioncited by 0
  3. 2026The Geometry of Randomized Smoothing on Feasible Setscited by 0
  4. 2026Minimax Gaussian Mechanisms for Continual Machine Unlearningcited by 0