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Found 3 papers, 1 papers with code
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition
We show that the privacy guarantees may in fact differ significantly between the two sampling schemes.
PLAN: Variance-Aware Private Mean Estimation
Under a concentration assumption on $\mathcal{D}$, we show how to exploit skew in the vector $\boldsymbol{\sigma}$, obtaining a (zero-concentrated) differentially private mean estimate with $\ell_2$ error proportional to $\|\boldsymbol{\sigma}\|_1$.
Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch
Chan, Li, Shi, and Xu [PETS 2012] describe a differentially private version of the Misra-Gries sketch, but the amount of noise it adds can be large and scales linearly with the size of the sketch: the more accurate the sketch is, the more noise this approach has to add.
