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SoK: Differential Privacies

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arxiv 1906.01337 v6 pith:OAZHWFO3 submitted 2019-06-04 cs.CR

classification cs.CR
keywords privacyvariantscategoriesdefinitionsdifferentialcombineddatadefinition
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Managing Correlations in Data and Privacy Demand

    cs.CR 2025-09 conditional novelty 5.0 of 10

    AHDP, an add-remove heterogeneous differential privacy framework, protects both user data and the user's privacy demand, and correlation-agnostic mechanisms exist for mean, frequency, and linear regression estimation.

  2. Rao Differential Privacy

    stat.ML 2025-08 reject novelty 4.0 of 10

    Rao differential privacy replaces divergence-based privacy with the Fisher-Rao distance and derives a square-root composition rule, but its post-processing proof is flawed.

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