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.