Microsoft Technology Licensing, LLC

Shruti Shrikant Tople

Sebastian Tschiatschek

Olga Ohrimenko

Amit Kumar Sharma

Aditya Vithal Nori

A method of selecting data for privacy preserving machine learning comprises: storing training data from a first party, storing a machine learning model, and storing criteria from the first party or from another party. The method comprises filtering the training data to select a first part of the training data to be used to train the machine learning model and select a second part of the training data. The selecting is done by computing a measure, using the criteria, of the contribution of the data to the performance of the machine learning model.

Privacy enhanced machine learning

This disclosure describes methods and systems for protecting machine learning models against privacy attacks. A machine learning model may be trained using a set of training data and causal relationship data. The causal relationship data may describe a subset of features in the training data that have a causal relationship with the outcome. The machine learning model may learn a function that predicts an outcome based on the training data and the causal relationship data. A predefined privacy guarantee value may be received. An amount of noise may be added to the machine learning model to make a privacy guarantee value of the machine learning model equivalent to or stronger than the predefined privacy guarantee value. The amount of noise may be added at a parameter level of the machine learning model.

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Cambridge, United Kingdom

Shruti Shrikant Tople