Microsoft Technology Licensing, LLC

Harsha Prasad Nori

Ruth Kikin-Gil

Christopher Lee Weider

Joshua Stanley Allen

Bolin Ding

Josh D Benaloh

Jacob Rubin Lorch

Sergey M Yekhanin

Melissa Erin Chase

Paul Luo Li

Srinath T V Setty

Olga Ohrimenko

Janardhan Dattatreya Kulkarni

A method and system for providing data imbalance detection and validation for a trained a ML model includes receiving a request to perform data imbalance detection on the trained ML model, identifying a feature of a dataset associated with the trained model for which data imbalance detection is to be performed, receiving access to the dataset, receiving access to the trained ML model, examining at least one of the dataset or outcome data generated by the trained ML model to determine a distribution of the feature or a distribution of the outcome data, and determining if the trained ML model exhibits data imbalance based at least in part on the distribution of the feature or the distribution of the outcome data.

Remote validation of machine-learning models for data imbalance

A method and system for performing semi or fully automatic data imbalance detection and correction in training a machine-learning (ML) model includes receiving a request to train the ML model, receiving access to a dataset for use in training the ML model, identifying a feature of the dataset for which data imbalance detection is to be performed, examining the dataset to determine a distribution of the feature across the dataset, determining if the distribution of the feature across the dataset indicates data imbalance, upon determining that the distribution of the feature across the dataset indicates data imbalance, identifying a desired distribution for the identified feature, selecting a subset of the dataset that corresponds with the selected feature and the desired distribution, and using the subset to train the ML model.

Method and system of performing data imbalance detection and correction in training a machine-learning model

A method and system for detecting imbalanced distribution of data that may signal bias in a dataset associated with training a machine-learning (ML) model includes receiving a request to perform data imbalance detection on the dataset associated with training the machine-learning (ML) model, identifying a feature of the dataset for which data imbalance detection is to be performed and examining the dataset to determine a distribution of the feature across the dataset. The result of the determination may then be presented in a user interface element to help identify data imbalance in the dataset.

Method and system of detecting data imbalance in a dataset used in machine-learning

This document relates to hardware protection of differential privacy techniques. One example obtains multiple instances of encrypted telemetry data within a secure enclave and processes the encrypted telemetry data to obtain multiple instances of unencrypted telemetry data. The example also processes, within the secure enclave, the multiple instances of unencrypted telemetry data to obtain a perturbed aggregate. The example also releases the perturbed aggregate from the secure enclave.

Hardware protection for differential privacy

Methods, systems, apparatuses, and computer-readable storage medium are described herein for remotely analyzing testing results based on LDP-based data obtained from client devices in order to determine an effect of a software application with respect to its features and/or the population in which the application is tested. The analysis is based on a series of statistical computations for conducting hypothesis tests to compare population means, while ensuring LDP for each user. For example, an LDP scheme is used on the client-side that privatizes a measured value corresponding to a usage of a resource of the client. A data collector receives the privatized data from two sets of populations. Each population's clients have a software application that may differ in terms of features or user group. The privatized data received from each population is analyzed to determine an effect of the difference between the software applications of the different populations.

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Harsha Prasad Nori