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  • Poisson_and_Differential_Privacy-4

    Submitted manuscript, 476 KB, PDF document

    Available under license: Other

  • Jackson_et_al_PSD_2024-accepted

    Accepted author manuscript, 566 KB, PDF document

    Embargo ends: 11/09/25

    Available under license: Other

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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/ISSN › Chapter (peer-reviewed) › peer-review

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Obtaining (Ɛ,δ)-differential privacy guarantees when using the Poisson distribution to synthesize tabular data. / Jackson, James; Mitra, Robin; Francis, Brian et al.
Privacy in Statistical Databases – PSD2024. ed. / Josep Domingo-Ferrer; Melek Önen. Cham: Springer, 2024. p. 102-112 (Lecture Notes in Computer Science; Vol. 14915).

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

Harvard

Jackson, J, Mitra, R, Francis, B & Dove, I 2024, Obtaining (Ɛ,δ)-differential privacy guarantees when using the Poisson distribution to synthesize tabular data. in J Domingo-Ferrer & M Önen (eds), Privacy in Statistical Databases – PSD2024. Lecture Notes in Computer Science, vol. 14915, Springer, Cham, pp. 102-112. https://doi.org/10.1007/978-3-031-69651-0_7

APA

Jackson, J., Mitra, R., Francis, B., & Dove, I. (2024). Obtaining (Ɛ,δ)-differential privacy guarantees when using the Poisson distribution to synthesize tabular data. In J. Domingo-Ferrer, & M. Önen (Eds.), Privacy in Statistical Databases – PSD2024 (pp. 102-112). (Lecture Notes in Computer Science; Vol. 14915). Springer. https://doi.org/10.1007/978-3-031-69651-0_7

Vancouver

Jackson J, Mitra R, Francis B, Dove I. Obtaining (Ɛ,δ)-differential privacy guarantees when using the Poisson distribution to synthesize tabular data. In Domingo-Ferrer J, Önen M, editors, Privacy in Statistical Databases – PSD2024. Cham: Springer. 2024. p. 102-112. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-69651-0_7

Author

Jackson, James ; Mitra, Robin ; Francis, Brian et al. / Obtaining (Ɛ,δ)-differential privacy guarantees when using the Poisson distribution to synthesize tabular data. Privacy in Statistical Databases – PSD2024. editor / Josep Domingo-Ferrer ; Melek Önen. Cham : Springer, 2024. pp. 102-112 (Lecture Notes in Computer Science).

Bibtex

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title = "Obtaining (Ɛ,δ)-differential privacy guarantees when using the Poisson distribution to synthesize tabular data",
abstract = "We show that differential privacy type guarantees can be obtained when using a Poisson synthesis mechanism to protect counts incontingency tables. Specifically, we show how to obtain (ϵ, δ)-probabilisticdifferential privacy guarantees via the Poisson distribution{\textquoteright}s cumulativedistribution function). We demonstrate this Poisson synthesis mechanismempirically with the synthesis of the ESCrep data set, an administrativetype database that resembles the English School Census.",
keywords = "Differential Privacy, Synthetic Data, tabular data",
author = "James Jackson and Robin Mitra and Brian Francis and Iain Dove",
year = "2024",
month = sep,
day = "12",
doi = "10.1007/978-3-031-69651-0_7",
language = "English",
isbn = "9783031696503",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "102--112",
editor = "Josep Domingo-Ferrer and Melek {\"O}nen",
booktitle = "Privacy in Statistical Databases – PSD2024",

}

RIS

TY - CHAP

T1 - Obtaining (Ɛ,δ)-differential privacy guarantees when using the Poisson distribution to synthesize tabular data

AU - Jackson, James

AU - Mitra, Robin

AU - Francis, Brian

AU - Dove, Iain

PY - 2024/9/12

Y1 - 2024/9/12

N2 - We show that differential privacy type guarantees can be obtained when using a Poisson synthesis mechanism to protect counts incontingency tables. Specifically, we show how to obtain (ϵ, δ)-probabilisticdifferential privacy guarantees via the Poisson distribution’s cumulativedistribution function). We demonstrate this Poisson synthesis mechanismempirically with the synthesis of the ESCrep data set, an administrativetype database that resembles the English School Census.

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

KW - Differential Privacy

KW - Synthetic Data

KW - tabular data

U2 - 10.1007/978-3-031-69651-0_7

DO - 10.1007/978-3-031-69651-0_7

M3 - Chapter (peer-reviewed)

SN - 9783031696503

T3 - Lecture Notes in Computer Science

SP - 102

EP - 112

BT - Privacy in Statistical Databases – PSD2024

A2 - Domingo-Ferrer, Josep

A2 - Önen, Melek

PB - Springer

CY - Cham

ER -