func TestCompositionMonotonicity(t *testing.T) · SOURCE AND DERIVATION. For the Gaussian mechanism with noise multiplier sigma and no subsampling (q

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This relation defines SOURCE AND DERIVATION. For the Gaussian mechanism with noise multiplier sigma and no subsa in the source section “func TestCompositionMonotonicity(t *testing.T) · SOURCE AND DERIVATION. For the Gaussian mechanism with noise multiplier sigma and no subsampling (q”. 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.

Terms & definitions

The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.

How it works

1. Read SOURCE AND DERIVATION. For the Gaussian mechanism with noise multiplier sigma and no subsa as the quantity being defined or updated by this relation.

2. Substitute the source-defined variables or recorded values and follow the displayed arithmetic and indexing exactly.

3. Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.

Assumptions & limits

• This source expression was recovered from the pinned code revision. Its presence does not establish that the associated program was built, run, or scientifically validated.

Original context

		prev = eps
	}
}

// TestPublishedGoogleRDPAccountant compares the accountant against a published Google differential privacy
// RDP accountant number, QUOTED AS A FIXED CONSTANT (never by calling any external tool).
//
// SOURCE AND DERIVATION. For the Gaussian mechanism with noise multiplier sigma and no subsampling (q = 1),
// the Renyi differential privacy at order alpha is alpha / (2 * sigma^2), and the Google dp_accounting
// RdpAccountant converts accumulated RDP to (epsilon, delta) by epsilon = min over alpha of
// rdp(alpha) + log(1/delta) / (alpha - 1) (Mironov 2017, Proposition 3 and Corollary 3; the same formula the
// Google library uses in python/dp_accounting/rdp/rdp_privacy_accountant.py). Cited in docs/AI_RESEARCH.md,
// the federated learning with differential privacy section:
//
//	https://github.com/google/differential-privacy/tree/main/python/dp_accounting
//	Mironov, Renyi Differential Privacy, CSF 2017, arXiv:1702.07476.
//
// FIXED PUBLISHED CONSTANT. For sigma = 1.0, a single Gaussian query (no subsampling), delta = 1e-5, the

SOURCE AND DERIVATION. For the Gaussian mechanism with noise multiplier sigma and no subsampling (q = 1),

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