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2022 | Book

Guide to Differential Privacy Modifications

A Taxonomy of Variants and Extensions

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About this book

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.

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
This chapter gives a short history of data privacy notions preceding differential privacy – the central idea of this Brief – and highlights the extent to it has been adopted.
Balázs Pejó, Damien Desfontaines
Chapter 2. Differential Privacy
Abstract
This chapter recaps the original differential privacy definition and introduces the seven dimensions detailed in this Brief to show how it can be modified or extended.
Balázs Pejó, Damien Desfontaines
Chapter 3. Quantification of Privacy Loss (Q)
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
Balázs Pejó, Damien Desfontaines
Chapter 4. Neighborhood Definition (N)
Abstract
Differential privacy considers datasets differing in one record. In many scenarios, it makes sense to protect a different property about their dataset, and all one has to do is change the definition of neighborhood. This chapter of the Brief gives an overview of the corresponding notions.
Balázs Pejó, Damien Desfontaines
Chapter 5. Variation of Privacy Loss (V)
Abstract
Differential privacy offers the same protection for all records. In practice, some records might require a higher level of protection. This chapter of the Brief gives an overview of the corresponding notions.
Balázs Pejó, Damien Desfontaines
Chapter 6. Background Knowledge (B)
Abstract
Differential privacy implicitly assumes that the attacker fully knows the dataset. This assumption can be seen as unrealistic, and it is natural to consider weaker adversaries. This chapter of the Brief gives an overview of the corresponding notions.
Balázs Pejó, Damien Desfontaines
Chapter 7. Change in Formalism (F)
Abstract
Differential privacy uses indistinguishability to compare the distribution of outputs given two neighboring inputs. Other formalisms have been proposed, some of which model the attacker more explicitly. This chapter of the Brief provides an overview of the corresponding notions.
Balázs Pejó, Damien Desfontaines
Chapter 8. Knowledge Gain Relativization (R)
Abstract
Differential privacy ensures that not more is revealed than a fixed amount of probabilistic information. Instead, one can explicitly take into account other ways data can leak. This chapter of the Brief gives an overview of the corresponding notions.
Balázs Pejó, Damien Desfontaines
Chapter 9. Computational Power (C)
Abstract
Differential privacy implicitly assumes that the attacker has infinite computing power. This is unrealistic in practice, so it is natural to consider attackers with only polynomial computing power. This chapter of the Brief gives an overview of the corresponding notions.
Balázs Pejó, Damien Desfontaines
Chapter 10. Summarizing Table
Abstract
This chapter summarizes the seven dimensions of this Brief into tables where the known relations and the related properties are listed.
Balázs Pejó, Damien Desfontaines
Chapter 11. Scope and Related Work
Abstract
This chapter details the criteria for inclusion of different data privacy definitions from this Brief and lists related works and existing surveys in the field of data privacy.
Balázs Pejó, Damien Desfontaines
Chapter 12. Conclusion
Abstract
This chapter contains the conclusion of this Brief.
Balázs Pejó, Damien Desfontaines
Metadata
Title
Guide to Differential Privacy Modifications
Authors
Balázs Pejó
Damien Desfontaines
Copyright Year
2022
Electronic ISBN
978-3-030-96398-9
Print ISBN
978-3-030-96397-2
DOI
https://doi.org/10.1007/978-3-030-96398-9

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