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SoK: Differential Privacies
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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.
Forward citations
Cited by 4 Pith papers
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Differentially Private SQL with Bounded User Contribution
A production-oriented SQL engine enforces user-level differential privacy for databases with arbitrary row counts per user via per-user contribution bounding, two-stage aggregation, and thresholding.
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Managing Correlations in Data and Privacy Demand
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.
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Rao Differential Privacy
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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Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data
Fairness metrics computed on differentially private synthetic data differ from real-data values by up to 0.32 for some measures, despite staying below 0.1 on average, across Adult, COMPAS, and Diabetes.
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