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...

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[0016]In a seventh aspect, the disclosure describes a memory with a model stored thereon. The model is generated according to a method. The method includes receiving, by a managing computing device, a plurality of datasets. Each dataset of the plurality of datasets is received from a respective client computing device of a plurality of client computing devices. Each dataset corresponds to a set of recorded values. Each dataset includes objects. The method also includes determining, by the managing computing device, a respective list of identifiers for each dataset and a composite list of identifiers including a combination of the lists of identifiers of each dataset of the plurality of datasets. Further, the method includes determining, by the managing computing device, a list of unique objects from among the plurality of datasets. In addition, the method includes selecting, by the managing computing device, a subset of identifiers from the composite list of identifiers. The method additionally includes determining, by the managing computing device, a subset of the list of unique objects corresponding to each identifier in the subset of identifiers. Still further, the method includes computing, by the managing computing device, a shared representation of the datasets based on the subset of the list of unique objects and a shared function having one or more shared parameters. Even further, the method includes determining, by the managing computing device, a sublist of objects for the respective dataset of each client computing device based on an intersection of the subset of identifiers with the list of identifiers for the respective dataset. Still even further, the method includes determining, by the managing computing device, a partial representation for the respective dataset of each client computing device based on the sublist of objects for the respective dataset and the shared representation. Even yet further, the method includes transmitting, by the managing computing device, to each of the client computing devices the sublist of objects for the respective dataset and the partial representation for the respective dataset. Yet further, the method includes receiving, by the managing computing device, one or more feedback values from at least one of the client computing devices. The one or more feedback values are determined by the client computing devices by determining, by the respective client computing device, a set of predicted values corresponding to the respective dataset. The set of predicted values is based on the partial representation and an individual function with one or more individual parameters corresponding to the respective dataset. In addition, the one or more feedback values are also determined by the client computing devices by determining, by the respective client computing device, an error for the respective dataset based on an individual loss function for the respective dataset, the set of predicted values corresponding to the respective dataset, the sublist of objects, and non-empty entries in the set of recorded values corresponding to the respective dataset. Even further, the one or more feedback values are also determined by the client computing devices by updating, by the respective client computing device, the one or more individual parameters for the respective dataset. Still further, the one or more feedback values are also determined by the client computing devices by determining, by the respective client computing device, the one or more feedback values, wherein the one or more feedback values are used to determine a change in the partial representation that corresponds to an improvement in the set of predicted values. The method also includes determining, by the managing computing device, based on the sublists of objects and the one or more feedback values from the client computing devices, one or more aggregated feedback values. Yet still further, the method includes updating, by the managing computing device, the one or more shared parameters based on the one or more aggregated feedback values. Yet even further, the method includes storing, by the managing computing device, the shared representation, the shared function, and the one or more shared parameters on the memory.

[0017]In an eighth aspect, the disclosure describes a method. The method includes receiving, by a managing computing device, a plurality of datasets. Each dataset of the plurality of datasets is received from a respective client computing device of a plurality of client computing devices. Each dataset corresponds to a set of recorded values. Each dataset includes objects. The method also includes determining, by the managing computing device, a respective list of identifiers for each dataset and a composite list of identifiers including a combination of the lists of identifiers of each dataset of the plurality of datasets. Further, the method includes determining, by the managing computing device, a list of unique objects from among the plurality of datasets. In addition, the method includes selecting, by the managing computing device, a subset of identifiers from the composite list of identifiers. The method additionally includes determining, by the managing computing device, a subset of the list of unique objects corresponding to each identifier in the subset of identifiers. Still further, the method includes computing, by the managing computing device, a shared representation of the datasets based on the subset of the list of unique objects and a shared function having one or more shared parameters. Even further, the method includes determining, by the managing computing device, a sublist of objects for the respective dataset of each client computing device based on an intersection of the subset of identifiers with the list of identifiers for the respective dataset. Still even further, the method includes determining, by the managing computing device, a partial representation for the respective dataset of each client computing device based on the sublist of objects for the respective dataset and the shared representation. Even yet further, the method includes transmitting, by the managing computing device, to each of the client computing devices the sublist of objects for the respective dataset and the partial representation for the respective dataset. Yet further, the method includes receiving, by the managing computing device, one or more feedback values from at least one of the client computing devices. The one or more feedback values are determined by the client computing devices by determining, by the respective client computing device, a set of predicted values corresponding to the respective dataset. The set of predicted values is based on the partial representation and an individual function with one or more individual parameters corresponding to the respective dataset. In addition, the one or more feedback values are also determined by the client computing devices by determining, by the respective client computing device, an error for the respective dataset based on an individual loss function for the respective dataset, the set of predicted values corresponding to the respective dataset, the sublist of objects, and non-empty entries in the set of recorded values corresponding to the respective dataset. Even further, the one or more feedback values are also determined by the client computing devices by updating, by the respective client computing device, the one or more individual parameters for the respective dataset. Still further, the one or more feedback values are also determined by the client computing devices by determining, by the respective client computing device, the one or more feedback values, wherein the one or more feedback values are used to determine a change in the partial representation that corresponds to an improvement in the set of predicted values. The method also includes determining, by the managing computing device, based on the sublists of objects and the one or more feedback values from the client computing devices, one or more aggregated feedback values. Yet still further, the method includes updating, by the managing computing device, the one or more shared parameters based on the one or more aggregated feedback values. Yet even further, the method includes using, by a computing device, the shared representation, the shared function, or the one or more shared parameters to determine an additional set of predicted values corresponding to a dataset.

[0018]In a ninth aspect, the disclosure describes a server device. The server device has instructions stored thereon that, when executed by a processor, perform a method. The method includes receiving a plurality of datasets. Each dataset of the plurality of datasets is received from a respective client computing device of a plurality of client computing devices. Each dataset corresponds to a set of recorded values. Each dataset includes objects. The method also includes determining a respective list of identifiers for each dataset and a composite list of identifiers that includes a combination of the lists of identifiers of each dataset of the plurality of datasets. Further, the method includes determining a list of unique objects from among the plurality of datasets. In addition, the method includes selecting a subset of identifiers from the composite list of identifiers. Still further, the method includes determining a subset of the list of unique objects corresponding to each identifier in the subset of identifiers. The method additionally includes computing a shared representation of the datasets based on the subset of the list of unique objects and a shared function having one or more shared parameters. Even further, the method includes determining a sublist of objects for the respective dataset of each client computing device based on an intersection of the subset of identifiers with the list of identifiers for the respective dataset. Yet further, the method includes determining a partial representation for the respective dataset of each client computing device based on the sublist of objects for the respective dataset and the shared representation. Even still further, the method includes transmitting to each of the client computing devices: the sublist of objects for the respective dataset and the partial representation for the respective dataset. Yet still further, the method includes receiving one or more feedback values from at least one of the client computing devices. The one or more feedback values are determined by the client computing devices by determining, by the respective client computing device, a set of predicted values corresponding to the respective dataset. The set of predicted values is based on the partial representation and an individual function with one or more individual parameters corresponding to the respective dataset. The one or more feedback values are also determined by the client computing devices by determining, by the respective client computing device, an error for the respective dataset based on an individual loss function for the respective dataset, the set of predicted values corresponding to the respective dataset, the sublist of objects, and non-empty entries in the set of recorded values corresponding to the respective dataset. Further, the one or more feedback values are determined by the client computing devices by updating, by the respective client computing device, the one or more individual parameters for the respective dataset. In addition, the one or more feedback values are determined by the client computing devices by determining, by the respective client computing device, the one or more feedback values. The one or more feedback values are used to determine a change in the partial representation that corresponds to an improvement in the set of predicted values. Even yet further, the method includes determining based on the sublists of objects and the one or more feedback values from the client computing devices, one or more aggregated feedback values. Still yet further, the method includes updating the one or more shared parameters based on the one or more aggregated feedback values.

[0019]In a tenth aspect, the disclosure

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