Interpretability framework for differentially private deep learning

A method comprising:

• receiving a dataset;
• receiving at least one first user-generated privacy parameter which governs a differential privacy (DP) algorithm to be applied to a function evaluated over the received dataset;
• calculating, based on the received at least one first user-generated privacy parameter, at least one second privacy parameter based on a ratio or overlap of probabilities of distributions of different observations;
• applying, using the at least one second privacy parameter, the DP algorithm to the function over the received dataset to result in an anonymized function output, the DP algorithm using the at least one second privacy parameter during execution of one or more steps involved in converting the received dataset into the anonymized function output;
• and
• anonymously training at least one machine learning model using the dataset after application of the DP algorithm to the function over the received dataset which, when deployed, is configured to classify input data.

A non-transitory machine-readable storage medium having embodied thereon instructions executable by one or more machines to perform operations comprising:

• receiving a dataset;
• receiving at least one first user-generated privacy parameter which governs a differential privacy (DP) algorithm to be applied to a function evaluated over the received dataset;
• calculating, based on the received at least one first user-generated privacy parameter, at least one second privacy parameter based on a ratio or overlap of probabilities of distributions of different observations;
• applying, using the at least one second privacy parameter, the DP algorithm to the function over the received dataset to result in an anonymized function output, the DP algorithm using the at least one second privacy parameter during execution of one or more steps involved in converting the received dataset into the anonymized function output;
• and
• anonymously training at least one machine learning model using the dataset after application of the DP algorithm to the function over the received dataset which, when deployed, is configured to classify input data.