Via Science, Inc.

John Christopher Muddle

Jeremy Taylor

Jesús Alejandro Cárdenes Cabré

Kai Chung Cheung

Colin Gounden

A computing system that is configured to receive requests to send computer executable programs to a data owner system associated with a data source for execution of the computer executable program by the data owner system. The data owner system may store to a blockchain a permitted list of programming functions, function libraries, function syntax definitions, and execution environment requirements. The computing system may be further configured to retrieve the permitted lists. The computing system may be further configured to evaluate the computer executable program using the permitted lists to determine if the computer executable program may be executed by the data owner system. The evaluation may be performed by generating an abstract syntax tree of the computer executable program. The computing system may be further configured to send the computer executable program to the data owner system if the computer executable program satisfies the conditions of the permitted lists.

Permissible code analysis

A computing system that facilitates approval and validation of executable code between parties. A template including executable code and specifying certain operations and functions to be performed on protected data, as well as constraints thereto, may be verified and agreed upon by parties. The verified template and/or a hash of the verified template may be stored on a blockchain. Prior to execution of the code certain parameters within the template may be filled and validated by a system that will execute the code. A contract, which too may be agreed upon and stored on the blockchain, may also include other terms governing the parties. The filled template may also be validated, and compared against a blockchain version of the template, by the parties prior to execution of the code and prior to access being granted to protected data. Such verifications and validations ensure that data is only operated on, using a secure system, within the parameters as agreed upon by the parties.

Verified templates

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.

Growing community of inventors

Montreal, Canada

John Christopher Muddle