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OverviewShortly 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 collectingexisting ones. Full Product DetailsAuthor: Balázs Pejó , Damien DesfontainesPublisher: Springer Nature Switzerland AG Imprint: Springer Nature Switzerland AG Edition: 1st ed. 2022 Weight: 0.168kg ISBN: 9783030963972ISBN 10: 3030963977 Pages: 89 Publication Date: 10 April 2022 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand ![]() We will order this item for you from a manufactured on demand supplier. Table of Contents1. 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.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |