Privacy filters and odometers for deep learning

A method for differentially private (DP) deep learning (DL) model generation, the method comprising:

• instantiating a privacy odometer with a nested privacy filter architecture, the nested privacy filter architecture including nested privacy filters of different, increasing sizes;
• training a DL model;
• maintaining, during the training and by the privacy odometer that operates using the nested privacy filters, a running total of privacy loss budget consumed by the training;
• and
• responsive to a query for the running total of the privacy loss budget consumed, returning, by the privacy odometer, a size of a smallest privacy filter of the nested privacy filters that is bigger than the running total of the privacy loss budget.

A non-transitory machine-readable medium including instructions that, when executed by a machine, cause the machine to perform operations for differentially private (DP) deep learning (DL) model generation, the operations comprising:

• instantiating a privacy odometer with a nested privacy filter architecture, the nested privacy filter architecture including nested privacy filters of different, increasing sizes;
• maintaining, during training of a DL model and by the privacy odometer that operates using the nested privacy filters, a running total of privacy loss budget consumed by the training;
• responsive to a query, returning (i) a size of a smallest privacy filter of the nested privacy filters that is bigger than the running total of the privacy loss budget and (ii) an overall training set accuracy of the DL model;
• and
• responsive to receiving data indicating to halt the training of the DL model, terminating the training of the DL model.