Privacy-preserving access and use of AI models using private data sets

Abstract

A privacy-preserving method of accessing and using an AI model. That access and use is provided as a service in association with a linked data operating environment. In this environment, applications have secure and permissioned access in an interoperable manner to private data that is stored in one or more online private data stores. The AI model is trained using one or more access sets of private data that are stored in the linked data operating environment. Typically, the model (e.g., a language model, an image-generation (diffusion) model, or the like) is uniquely associated with an entity whose access set of private data is used for model training. To facilitate multi-use training and use, the model comprises a base model that is fine-tuned using the private data access set to generate a fine-tuned model. The fine-tuned model can be further tuned efficiently as data in the underlying access sets changes.

Dependencies are inferred from the claim text.