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2022 | OriginalPaper | Chapter

3. Quantification of Privacy Loss (Q)

Authors : Balázs Pejó, Damien Desfontaines

Published in: Guide to Differential Privacy Modifications

Publisher: Springer International Publishing

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Abstract

Differential privacy gives a worst-case guarantee as it quantifies all possible neighboring datasets and overall possible outputs. It is natural to consider relaxations, especially since they often have better composition properties. This chapter of the Brief gives an overview of the corresponding notions

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previous chapter Differential Privacy
next chapter Neighborhood Definition (N)
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Metadata
Title
Quantification of Privacy Loss (Q)
Authors
Balázs Pejó
Damien Desfontaines
Copyright Year
2022
Book
Guide to Differential Privacy Modifications

Print ISBN: 978-3-030-96397-2

Electronic ISBN: 978-3-030-96398-9

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-96398-9_3

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