Machine learning model validation and authentication

This invention describes a way to protect machine learning models from being misused or attacked. It works by encrypting the model's internal settings using a special key tied to an authorized user. The claims specifically narrow this to using homomorphic encryption for the model's settings, and potentially for the data fed into the model as well.

Why it matters: Since 2020, the widespread deployment of powerful and often proprietary machine learning models, including generative AI, has dramatically amplified concerns over model security, intellectual property, and malicious exploitation. The focus on homomorphic encryption for model parameters directly addresses the heightened need to protect these valuable assets while maintaining their utility.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedComplex software, homomorphic encryption implementation

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