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Guide to differential privacy modifi...

Pejo, Balazs.

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Guide to differential privacy modifications = a taxonomy of variants and extensions /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Guide to differential privacy modifications/ by Balazs Pejo, Damien Desfontaines.
Reminder of title:	a taxonomy of variants and extensions /
Author:	Pejo, Balazs.
other author:	Desfontaines, Damien.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	viii, 89 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. Diﬀerential Privacy -- 3. Quantiﬁcation of privacy loss -- 4. Neighborhood deﬁnition (N) -- 5. Variation of privacy loss (V) -- 6. Background knowledge (B) -- 7. Change in formalism (F) -- 8. Relativization of the knowledge gain (R) -- 9. Computational power (C) -- 10. Summarizing table -- 11. Scope and related work -- 12. Conclusion.
Contained By:	Springer Nature eBook
Subject:	Computer security. -
Online resource:	https://doi.org/10.1007/978-3-030-96398-9
ISBN:	9783030963989

Guide to differential privacy modifications = a taxonomy of variants and extensions /
Pejo, Balazs.

Guide to differential privacy modificationsa taxonomy of variants and extensions /[electronic resource] :by Balazs Pejo, Damien Desfontaines. - Cham :Springer International Publishing :2022. - viii, 89 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

1. Introduction -- 2. Diﬀerential Privacy -- 3. Quantiﬁcation of privacy loss -- 4. Neighborhood deﬁnition (N) -- 5. Variation of privacy loss (V) -- 6. Background knowledge (B) -- 7. Change in formalism (F) -- 8. Relativization of the knowledge gain (R) -- 9. Computational power (C) -- 10. Summarizing table -- 11. Scope and related work -- 12. Conclusion.

Shortly after it was ﬁrst introduced in 2006, diﬀerential privacy became the ﬂagship data privacy deﬁnition. Since then, numerous variants and extensions were proposed to adapt it to diﬀerent scenarios and attacker models. In this work, we propose a systematic taxonomy of these variants and extensions. We list all data privacy deﬁnitions based on diﬀerential privacy, and partition them into seven categories, depending on which aspect of the original deﬁnition is modiﬁed. These categories act like dimensions: Variants from the same category cannot be combined, but variants from diﬀerent categories can be combined to form new deﬁnitions. We also establish a partial ordering of relative strength between these notions by summarizing existing results. Furthermore, we list which of these deﬁnitions satisfy some desirable properties, like composition, post-processing, and convexity by either providing a novel proof or collecting existing ones.

ISBN: 9783030963989

Standard No.: 10.1007/978-3-030-96398-9doiSubjects--Topical Terms:

540555
Computer security.

LC Class. No.: QA76.9.A25 / P45 2022

Dewey Class. No.: 005.8

Guide to differential privacy modifications = a taxonomy of variants and extensions /
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505 0 $a 1. Introduction -- 2. Diﬀerential Privacy -- 3. Quantiﬁcation of privacy loss -- 4. Neighborhood deﬁnition (N) -- 5. Variation of privacy loss (V) -- 6. Background knowledge (B) -- 7. Change in formalism (F) -- 8. Relativization of the knowledge gain (R) -- 9. Computational power (C) -- 10. Summarizing table -- 11. Scope and related work -- 12. Conclusion.
520 $a Shortly after it was ﬁrst introduced in 2006, diﬀerential privacy became the ﬂagship data privacy deﬁnition. Since then, numerous variants and extensions were proposed to adapt it to diﬀerent scenarios and attacker models. In this work, we propose a systematic taxonomy of these variants and extensions. We list all data privacy deﬁnitions based on diﬀerential privacy, and partition them into seven categories, depending on which aspect of the original deﬁnition is modiﬁed. These categories act like dimensions: Variants from the same category cannot be combined, but variants from diﬀerent categories can be combined to form new deﬁnitions. We also establish a partial ordering of relative strength between these notions by summarizing existing results. Furthermore, we list which of these deﬁnitions satisfy some desirable properties, like composition, post-processing, and convexity by either providing a novel proof or collecting existing ones.
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