Natural f-DP filters are invalid for adaptive composition, but a CLT-based GDP approximation gives tighter bounds than RDP for subsampled Gaussians when sampling rate is near 0 or 1.
A privacy profileH − M ofMunder remove adjacency implies that the privacy profileH M is HM(γ) = max H − M(γ),1−γ+γH − M 1 γ
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$f$-Differential Privacy Filters: Validity and Approximate Solutions
Natural f-DP filters are invalid for adaptive composition, but a CLT-based GDP approximation gives tighter bounds than RDP for subsampled Gaussians when sampling rate is near 0 or 1.