Recognition: unknown
Concentrated Differential Privacy
classification
💻 cs.DS
cs.CR
keywords
privacydifferentialconcentratedrelaxationaccuracybettercompromisingcomputations
read the original abstract
We introduce Concentrated Differential Privacy, a relaxation of Differential Privacy enjoying better accuracy than both pure differential privacy and its popular "(epsilon,delta)" relaxation without compromising on cumulative privacy loss over multiple computations.
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Forward citations
Cited by 2 Pith papers
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Barriers to Counterfactual Credit Attribution for Autoregressive Models
CCA does not compose autoregressively and retrofitting requires exponential query complexity under weak optimality.
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Beyond Membership: Limitations of Add/Remove Adjacency in Differential Privacy
Add/remove adjacency in DP overstates attribute privacy relative to substitute adjacency; new auditing attacks confirm inconsistency with add/remove reports but consistency with substitute accounting.
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