Secure Broker-Mediated Data Analysis and Prediction

Abstract

The present disclosure relates to secure broker-mediated data analysis and prediction. One example embodiment includes a method. The method includes receiving, by a managing computing device, a plurality of datasets from client computing devices. The method also includes computing, by the managing computing device, a shared representation based on a shared function having one or more shared parameters. Further, the method includes transmitting, by the managing computing device, the shared representation and other data to the client computing devices. In addition, the method includes, based on the shared representation and the other data, the client computing devices update partial representations and individual functions with one or more individual parameters. Still further, the method includes determining, by the client computing devices, feedback values to provide to the managing computing device. Additionally, the method includes updating, by the managing computing device, the one or more shared parameters based on the feedback values.

Description

BACKGROUND[0001]Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.[0002]Machine learning is a branch of computer science that seeks to automate the building of an analytical model. In machine learning, algorithms are used to create models that “learn” using datasets. Once “taught”, the machine-learned models may be used to make predictions about other datasets, including future datasets. Machine learning has proven useful for developing models in a variety of fields. For example, machine learning has been applied to computer vision, statistics, data analytics, bioinformatics, deoxyribose nucleic acid (DNA) sequence identification, marketing, linguistics, economics, advertising, speech recognition, gaming, etc.[0003]Machine learning involves training the model on a set of data, usually called “training data.” Training the model may include two...

AI Technical Summary

Benefits of technology

The patent describes a method for securely analyzing and predicting data from multiple parties without revealing private information. The method involves receiving multiple datasets from client devices, combining them, and performing machine learning algorithms to update a shared model. The method can be carried out in an iterative manner and can be used for joint machine learning. The method can also be used for on-going data analysis and prediction. The patent also describes a system for managing the data analysis and prediction process. The technical effects of the patent include improved data analysis and prediction capabilities, improved data privacy, and improved efficiency and accuracy in data analysis and prediction.

Problems solved by technology

However, in some scenarios, attaining more training data may not be possible.

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