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

Inhalt

1. Introduction.- 2. Di erential Privacy.- 3. Quanti cation of privacy loss.- 4. Neighborhood de nition (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.