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Differential Privacy

class DpStep

Data for differentially private step.

Arguments:

  • noise_multiplier - The ratio of the standard deviation of the Gaussian noise to the L2-sensitivity of the gradients to which the noise is added (How much noise to add).
  • max_grad_norm - The maximum norm of the per-sample gradients. Any gradient with norm higher than this will be clipped to this value, thus limiting the L2-sensitivity.
  • max_batch_size - Maximum size of the physical batch processed during computations. It will not change the size of the logical batch. If <= 0, no cap is imposed on the physical batch. Notice that due to Poisson sampling, the logical batch size during differentially private training is distributed according to a binomial distribution.

class DpBudget

Differential privacy (DP) budget.

Arguments:

  • eps - Epsilon component of the DP budget.
  • delta - Delta component of the DP budget.