Patent ID: 11861476
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. An apparatus, comprising:
at least one processor; and
a computer readable storage medium having computer readable program code embodied therewith and executable by the at least one processor;
wherein the computer readable program code is configured to receive, at a service provider, a query from a user, wherein the service provider is connected with a plurality of data owners, each of the plurality of data owners having at least one dataset comprising information responsive to the query, wherein each of the plurality of data owners do not trust other of the plurality of data owners and the service provider;
wherein the computer readable program code is configured to provide, by the service provider, the query to each of the plurality of data owners, wherein each of the plurality of data owners has a local machine learning model trained using a dataset of a corresponding data owner, wherein each data owner evaluates the query by running a secure two-party computation protocol with the user facilitated by the service provider transmitting messages between the user and a corresponding of the plurality of data owners and wherein each data owner provides a commitment of the local machine learning model that is stored in a hash commitment directory for benchmarking, and wherein the plurality of data owners train a meta-model by a collaboration facilitated by the service provider using secure multi-party computation, wherein each of the plurality of data owners has a share of the meta-model;
wherein the computer readable program code is configured to secret share, via the service provider and using a multi-party computation algorithm to ensure security and privacy, model output from each of the plurality of data owners between the other of the plurality of data owners, wherein the model output comprises an output responsive to the query computed using the local machine learning model corresponding to the data owner and wherein the sharing comprises, each of the plurality of data owners, encrypting, to each other data owner, the model output corresponding to the data owner and encrypting the model output shared from the other of the plurality of data owners;
wherein the computer readable program code is configured to receive, at the service provider and from each of the plurality of data owners, a set of local meta-features corresponding to the query and comprising forward pass processing of the local machine learning model of a given of the plurality of data owners, post processing of the local machine learning model of the given of the plurality of data owners, and at least one neighborhood of the local machine learning model of the given of the plurality of data owners, wherein each of the set of the local meta-features are extracted, from the encrypted secret shares of the model outputs, by a corresponding of the plurality of data owners using meta-training samples from the local machine learning model and cluster centroids from other of the plurality of data owners; and
generate, using the service provider, a response to the query, wherein the generating comprises determining, by evaluating the meta-model using the set of local meta-features received from each of the plurality of data owners, weights for outputs from the local machine learning models and aggregating the outputs in view of the weights.