What is recorded here?
Represented privacy curves using privacy-loss random variables and composed them by FFT convolution, with mean-preserving discretization and truncation/error control for accurate privacy accounting.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits privacy-loss distributions and prior Fourier accountants; not invention of DP or convolution. Optimal composition means the supplied privacy curves/mechanism class, not auditing a real training implementation. Accuracy depends on mesh, tail truncation and input curves. Near-square-root self-composition complexity has accuracy/log factors and is not all heterogeneous workloads or machine-verified floating-point soundness.
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People and systems
Sivakanth Gopi; Yin Tat Lee; Lukas Wutschitz
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
PRV accounting: fast numerical composition with controlled approximation
Represented privacy curves using privacy-loss random variables and composed them by FFT convolution, with mean-preserving discretization and truncation/error control for accurate privacy accounting.
Scientific scope
Inherits privacy-loss distributions and prior Fourier accountants; not invention of DP or convolution. Optimal composition means the supplied privacy curves/mechanism class, not auditing a real training implementation. Accuracy depends on mesh, tail truncation and input curves. Near-square-root self-composition complexity has accuracy/log factors and is not all heterogeneous workloads or machine-verified floating-point soundness.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Sivakanth Gopi; Yin Tat Lee; Lukas Wutschitz
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.