Debiasing Functions of Private Statistics in Postprocessing

• URL: http://arxiv.org/abs/2502.13314v1
• Date: Tue, 18 Feb 2025 22:16:37 GMT
• Title: Debiasing Functions of Private Statistics in Postprocessing
• Authors: Flavio Calmon, Elbert Du, Cynthia Dwork, Brian Finley, Grigory Franguridi,
• Abstract summary: We derive an unbiased estimator for private means when the size $n$ of the dataset is not publicly known. We also apply our estimators to develop unbiased transformation mechanisms for per-record differential privacy.
• Score: 6.22153888560487
• License:
• Abstract: Given a differentially private unbiased estimate $\tilde{q}=q(D) +\nu$ of a statistic $q(D)$, we wish to obtain unbiased estimates of functions of $q(D)$, such as $1/q(D)$, solely through post-processing of $\tilde{q}$, with no further access to the confidential dataset $D$. To this end, we adapt the deconvolution method used for unbiased estimation in the statistical literature, deriving unbiased estimators for a broad family of twice-differentiable functions when the privacy-preserving noise $\nu$ is drawn from the Laplace distribution (Dwork et al., 2006). We further extend this technique to a more general class of functions, deriving approximately optimal estimators that are unbiased for values in a user-specified interval (possibly extending to $\pm \infty$). We use these results to derive an unbiased estimator for private means when the size $n$ of the dataset is not publicly known. In a numerical application, we find that a mechanism that uses our estimator to return an unbiased sample size and mean outperforms a mechanism that instead uses the previously known unbiased privacy mechanism for such means (Kamath et al., 2023). We also apply our estimators to develop unbiased transformation mechanisms for per-record differential privacy, a privacy concept in which the privacy guarantee is a public function of a record's value (Seeman et al., 2024). Our mechanisms provide stronger privacy guarantees than those in prior work (Finley et al., 2024) by using Laplace, rather than Gaussian, noise. Finally, using a different approach, we go beyond Laplace noise by deriving unbiased estimators for polynomials under the weak condition that the noise distribution has sufficiently many moments.

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