SWhat changed
Combined per-example gradient-norm clipping, calibrated Gaussian noise and privacy-loss moment accounting to train neural networks while tracking example-level differential privacy.
Scientific scope
Combined per-example gradient-norm clipping, calibrated Gaussian noise and privacy-loss moment accounting to train neural networks while tracking example-level differential privacy. Inherits DP and noisy SGD, adds deep-training implementation/accounting refinements. Guarantees require the stated neighboring-data definition, bounded influence, randomized sampling/noise and composition accounting; example-level DP does not automatically give comparable user-level DP. Analyzed independent inclusion sampling differs from practical shuffled batching. No universal zero-utility-loss or complete security claim.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Inherits DP and noisy SGD, adds deep-training implementation/accounting refinements. Guarantees require the stated neighboring-data definition, bounded influence, randomized sampling/noise and composition accounting; example-level DP does not automatically give comparable user-level DP. Analyzed independent inclusion sampling differs from practical shuffled batching. No universal zero-utility-loss or complete security claim.
Contributors
- Source-listed authors and technical contributors · SCIENCE: 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.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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