Via Science, Inc.

Colin Gounden

Jeremy Taylor

Jesús Alejandro Cárdenes Cabré

Kai Chung Cheung

Mathew Rogers

John Christopher Muddle

Madjid Aoudia

An access gateway may grant a requestor access to a computer resource. The requestor may receive a credential from an identity provider and calculate a zero-knowledge proof of possession of the credential. The requestor may use the proof to request access to the computer resource. The identity provider may record a policy corresponding to the credential in a distributed ledger. The access gateway may, subject to verifying the proof, retrieve the policy from the distributed ledger. The policy may indicate that the requestor is authorized to access the computer resource. The access gateway may grant the requestor access to the computer resource as indicated by the policy.

Access gateway system for accessing a resource

A request to train a neural-network model is received, as are a first offer to supply encrypted training data and a second offer to train the model. After determining that the offers satisfy the request, a model provider told to train the model. If they model performs in accordance with a quality metric, it is sent to a model user.

Secure data processing transactions

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

Computer instructions corresponding to a neural-network model are received and encrypted using an encryption technique. Training data encrypted using the encryption technique is received from a data source. The model is trained using the training data using, for example, a gradient descent technique. If the model performs in accordance with a quality metric, it is sent to a device of a model user.

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Cambridge, MA, United States of America

Colin Gounden