Privacy-Preserving AI and ML
Inpher enables users to build more predictive models while keeping user inputs private. Whether you are looking to train models or generate inference, Inpher’s privacy-preserving suite of products unlocks data across organizational silos through encryption-in-use technologies. Develop better machine learning models from new features and samples and unlock generative AI initiatives within your organization in a secure, private and compliant manner.
Run analytics or train a machine learning model on a single data set or multiple data sets in a collaboration in a way that requires no data transfer or exposure of data inputs.
Deliver high model accuracy and precision with encryption-in-use rather than existing privacy approaches that reduce predictive features or inject noise.
Generate prompts to a model securely and without a third-party through SecurAI. SecurAI provides attestation and permits users to generate inference off of their own private or proprietary data from a number of sources without risk of exposure to the model server.
TRUSTED BY GLOBAL INNOVATORS
“Working with Inpher to leverage their technology to access non-public data sources aligns with our objective to enhance our informational edge and generate value for the CPP Fund over the long run.”
Daniel WroblewskiManaging Director, Alpha Gen Lab, CPP Investments“We are excited to be partnering on a proposition that will deliver a secure, end-to-end consented data sharing solution leveraging Inpher’s patented technology, enterprise-ready platform and expert team for the next wave of innovation.”
Danny TyrrellCofounder, DataCo Technologies“The ‘bad guys’ have all the same technologies we do. But the one thing they cannot obtain is the scale of training data we could through collaborative sharing, such as through the potential Inpher offers.”
Hays W. “Skip” McCormickData Science Lead BNY Mellon
