Guide to Differential Privacy Modifications: A Taxonomy of Variants and Extensions

Differential privacy is a fundamental data privacy definition that has been extended and modified in various ways. This work provides a systematic taxonomy of these variants and extensions, categorizing them based on the aspect of the original definition that is modified. It also establishes a partial ordering of relative strength between these notions and lists which of them satisfy desirable properties such as composition, post-processing, and convexity.

Format: Paperback / softback
Length: 89 pages
Publication date: 10 April 2022
Publisher: Springer Nature Switzerland AG

Differential privacy is a fundamental concept in data privacy that was first introduced in 2006. Since then, it has become the flagship data privacy definition, with numerous variants and extensions 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, and 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, such as composition, post-processing, and convexity, by either providing a novel proof or collecting existing ones.

Differential privacy is a fundamental concept in data privacy that was first introduced in 2006. Since then, it has become the flagship data privacy definition, with numerous variants and extensions 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, and 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, such as composition, post-processing, and convexity, by either providing a novel proof or collecting existing ones.

Weight: 168g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030963972
Edition number: 1st ed. 2022