University of California

Ciphermode Labs, Inc.

Mohammad Sadegh Riazi

Farinaz Koushanfar

Ilya Razenshteyn

Mohsen Imani

Tajana Rosing

Yeseong Kim

Seyed Mohammad Chavoshian

The described technology is generally directed towards secure collaborative processing of private inputs. A secure execution engine can process encrypted data contributed by multiple parties, without revealing the encrypted data to any of the parties. The encrypted data can be processed according to any program written in a high-level programming language, while the secure execution engine handles cryptographic processing.

Secure collaborative processing of private inputs

In some embodiments, there is provided a system for generating synthetic human fingerprints. The system includes at least one processor and at least one memory storing instructions which when executed by the at least one processor causes operations, such as receiving, from a database and/or a sensor, at least one real fingerprint; training, based on the at least one real fingerprint, a generative adversarial network to learn a distribution of real fingerprints; training a super-resolution engine to learn to transform low resolution synthetic fingerprints to high-resolution fingerprints; providing to the trained super resolution engine at least one low resolution synthetic fingerprint that is generated as an output by the trained generative adversarial network; and in response to the providing, outputting, by trained super resolution engine, at least one high resolution synthetic fingerprint.

Synthetic human fingerprints

A computing system can include a plurality of clients located outside a cloud-based computing environment, where each of the clients may be configured to encode respective original data with a respective unique secret key to generate data hypervectors that encode the original data. A collaborative machine learning system can operate in the cloud-based computing environment and can be operatively coupled to the plurality of clients, where the collaborative machine learning system can be configured to operate on the data hypervectors that encode the original data to train a machine learning model operated by the collaborative machine learning system or to generate an inference from the machine learning model.

Systems, circuits and computer program products providing a framework for secured collaborative training using hyper-dimensional vector based data encoding/decoding and related methods

Growing community of inventors

Los Angeles, CA, United States of America

Mohammad Sadegh Riazi