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  • Poisson_and_Differential_Privacy-4

    Submitted manuscript, 476 KB, PDF document

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  • Jackson_et_al_PSD_2024-accepted

    Accepted author manuscript, 566 KB, PDF document

    Embargo ends: 11/09/25

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Obtaining (Ɛ,δ)-differential privacy guarantees when using the Poisson distribution to synthesize tabular data

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

Published
Publication date12/09/2024
Host publicationPrivacy in Statistical Databases – PSD2024
EditorsJosep Domingo-Ferrer, Melek Önen
Place of PublicationCham
PublisherSpringer
Pages102-112
Number of pages11
ISBN (electronic)9783031696510
ISBN (print)9783031696503
<mark>Original language</mark>English

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume14915
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Abstract

We show that differential privacy type guarantees can be obtained when using a Poisson synthesis mechanism to protect counts in
contingency tables. Specifically, we show how to obtain (ϵ, δ)-probabilistic
differential privacy guarantees via the Poisson distribution’s cumulative
distribution function). We demonstrate this Poisson synthesis mechanism
empirically with the synthesis of the ESCrep data set, an administrativetype database that resembles the English School Census.