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Documents authored by Du, Elbert

Document
DOI: 10.4230/LIPIcs.FORC.2025.17
Debiasing Functions of Private Statistics in Postprocessing

Authors: Flavio Calmon, Elbert Du, Cynthia Dwork, Brian Finley, and Grigory Franguridi

Published in: LIPIcs, Volume 329, 6th Symposium on Foundations of Responsible Computing (FORC 2025)

Abstract
Given a differentially private unbiased estimate q̃ = q(D) +ν 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 q̃, 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 - those that are tempered distributions - when the privacy-preserving noise ν is drawn from the Laplace distribution (Dwork et al., 2006). We further extend this technique to functions other than tempered distributions, deriving approximately optimal estimators that are unbiased for values in a user-specified interval (possibly extending to ± ∞). 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.
Cite as

Flavio Calmon, Elbert Du, Cynthia Dwork, Brian Finley, and Grigory Franguridi. Debiasing Functions of Private Statistics in Postprocessing. In 6th Symposium on Foundations of Responsible Computing (FORC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 329, pp. 17:1-17:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{calmon_et_al:LIPIcs.FORC.2025.17,
  author =	{Calmon, Flavio and Du, Elbert and Dwork, Cynthia and Finley, Brian and Franguridi, Grigory},
  title =	{{Debiasing Functions of Private Statistics in Postprocessing}},
  booktitle =	{6th Symposium on Foundations of Responsible Computing (FORC 2025)},
  pages =	{17:1--17:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-367-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{329},
  editor =	{Bun, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2025.17},
  URN =		{urn:nbn:de:0030-drops-231449},
  doi =		{10.4230/LIPIcs.FORC.2025.17},
  annote =	{Keywords: Differential privacy, deconvolution, unbiasedness}
}
Document
DOI: 10.4230/LIPIcs.FORC.2022.6
Improved Generalization Guarantees in Restricted Data Models

Authors: Elbert Du and Cynthia Dwork

Published in: LIPIcs, Volume 218, 3rd Symposium on Foundations of Responsible Computing (FORC 2022)

Abstract
Differential privacy is known to protect against threats to validity incurred due to adaptive, or exploratory, data analysis - even when the analyst adversarially searches for a statistical estimate that diverges from the true value of the quantity of interest on the underlying population. The cost of this protection is the accuracy loss incurred by differential privacy. In this work, inspired by standard models in the genomics literature, we consider data models in which individuals are represented by a sequence of attributes with the property that where distant attributes are only weakly correlated. We show that, under this assumption, it is possible to "re-use" privacy budget on different portions of the data, significantly improving accuracy without increasing the risk of overfitting.
Cite as

Elbert Du and Cynthia Dwork. Improved Generalization Guarantees in Restricted Data Models. In 3rd Symposium on Foundations of Responsible Computing (FORC 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 218, pp. 6:1-6:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{du_et_al:LIPIcs.FORC.2022.6,
  author =	{Du, Elbert and Dwork, Cynthia},
  title =	{{Improved Generalization Guarantees in Restricted Data Models}},
  booktitle =	{3rd Symposium on Foundations of Responsible Computing (FORC 2022)},
  pages =	{6:1--6:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-226-6},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{218},
  editor =	{Celis, L. Elisa},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2022.6},
  URN =		{urn:nbn:de:0030-drops-165299},
  doi =		{10.4230/LIPIcs.FORC.2022.6},
  annote =	{Keywords: Differential Privacy, Adaptive Data Analysis, Transfer Theorem}
}
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