Via Science, Inc.

Mathew Rogers

Jeremy Taylor

Kai Chung Cheung

Colin Gounden

Multiple systems may determine neural-network output data and neural-network parameter data and may transmit the data therebetween to train and run the neural-network model to predict an event given input data. A data-provider system may perform a dot-product operation using encrypted data, and a secure-processing component may decrypt and process that data using an activation function to predict an event. Multiple secure-processing components may be used to perform a multiplication operation using homomorphic encrypted data.

Multiple data source secure data processing

A first component determines encrypted data representing an event and encrypted threshold data corresponding to an outlier of the event. The first system may process the data using, for example, one or more composite integers, and may send the result to a second system. This second system may subtract the data to determine of the encrypted data is greater than, less than, or equal to the encrypted threshold. If so, the second system may determine that the encrypted data corresponds to an outlier of the data. The second system may send an indication of this determination to a third system.

Secure data processing

Multiple data sources encrypt data using encryption key data received from a first system; a second system does not have access to the encryption key data. The second system receives the encrypted data from the multiple data sources. Because the encryption is additively homomorphic, the second system may create encrypted summation data using the encrypted data. The second system may send the encrypted summation data to the first system, which may then decrypt the encrypted summation data to create unencrypted summation data.

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Montreal, Canada

Mathew Rogers