PRIVACY FILTERS AND ODOMETERS FOR DEEP LEARNING

Generally discussed herein are devices, systems, and methods for improving phishing webpage content detection. A method can include instantiating an odometer with a nested privacy filter architecture, the nested privacy filter including privacy filters of different, increasing sizes, training a DL model, maintaining, during training and by a privacy odometer that operates using the nested privacy filter, a running total of privacy loss budget consumed by the training, and responsive to a query for the total privacy loss budget consumed, returning, by the 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.