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This relation defines Gaussian mechanism depends only on the noise multiplier sigma in the source section “accountant · Gaussian mechanism depends only on the noise multiplier sigma”. It specifies the displayed relationship between the named quantities or parameters. Its interpretation depends on the source’s stated variables and conditions; this record does not assign an unprovided physical or cognitive meaning to them.
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Original context
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1.25, 1.5, 1.75, 2, 2.5, 3, 4, 5, 6, 8, 10, 12, 16, 20, 24, 32, 48, 64, 96, 128, 192, 256, 384, 512,
}
// GaussianRelease describes one Gaussian mechanism release for the accountant.
//
// CONVENTION (documented once, load bearing). The mechanism clips each per tenant contribution to L2 norm C
// (the sensitivity), then adds isotropic Gaussian noise of standard deviation NoiseSigma. The privacy of a
// Gaussian mechanism depends only on the noise multiplier sigma = NoiseSigma / C, the noise measured in
// units of the sensitivity. For an L2 sensitivity 1 mechanism (NoiseSigma already expressed in sensitivity
// units, C = 1) the multiplier is just NoiseSigma. The per release Renyi differential privacy at order alpha
// is the closed form alpha / (2 * sigma^2). This is the standard Gaussian mechanism RDP curve (Mironov 2017,
// cited in docs/AI_RESEARCH.md, the federated learning with differential privacy section).
type GaussianRelease struct {
NoiseSigma float64 // standard deviation of the added Gaussian noise, in the same units as the clip norm
ClipNorm float64 // the L2 clip norm C, the per tenant sensitivity; must be greater than zero
}
// noiseMultiplier returns sigma = NoiseSigma / ClipNorm, the noise measured in units of the sensitivity, theExact original notation
Gaussian mechanism depends only on the noise multiplier sigma = NoiseSigma / C, the noise measured in