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Fully Adaptive Composition for Gaussian Differential Privacy

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arxiv 2210.17520 v1 pith:OMJIKNNC submitted 2022-10-31 cs.CR cs.LG

Adam Smith , Abhradeep Thakurta This is my paper · ORCID
classification cs.CRcs.LG
keywords privacydifferentialgaussianadaptiveanalystcompositiondatafully
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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.

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  1. Adaptive Diffusion Denoised Smoothing : Certified Robustness via Randomized Smoothing with Differentially Private Guided Denoising Diffusion

    cs.CV 2025-07 conditional novelty 6.0 of 10

    ADDS certifies adversarial robustness of guided denoising diffusion models by composing per-step Gaussian differential privacy budgets with a privacy filter.

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