Two technologies — homomorphic encryption and federated learning — could allow companies and researchers to collaborate on data analyses and the creation of machine-learning models without actually exposing data to leakage risks, according to a pair of Intel researchers who spoke at the company virtual labs event on Thursday.
Federated learning allows collaborators to start with a single common machine-learning model, train that model on their own in-house data, and then securely collect and combine the now-divergent models to create a more accurate iteration that incorporates data from all participants. Homomorphic encryption is a more general approach, the fruit of a special field of cryptography that focuses on ways to protect the data but allow calculations to be done on the encrypted data — searching and training machine-learning algorithms, for example. This essentially protects privacy while maintaining the usefulness of the information.
Intel has doubled down on both technologies, building support for them into its hardware using the Software Guard Extensions (SGX). The result will be less expensive applications of the two technologies, said Jason Martin, principal engineer with the secure intelligence team at Intel, in a separate interview with Dark Reading.
"Unprocessed data is data that's not useful," he said. "The primary tools that we have for making sense of the increasing volumes of data is machine learning and statistics technologies, but companies are worried about sharing that data because of security and privacy concerns."
Intel's research and plans for the technology were revealed by Martin and Rosario Cammarota, a principal engineer focused on computing on encrypted data at Intel, intel doubles emerging technologies sharing using securely
