Model protection refers to methods used to secure artificial intelligence models against theft, unauthorized access, or manipulation. These techniques include watermarking, encryption, and the use of secure hardware environments. Its purpose is to preserve the integrity and intellectual property associated with the model’s design and parameters. This ensures the continued reliability of AI systems.
Context
As AI models gain significant commercial value, particularly in areas such as financial forecasting within digital asset markets, model protection becomes increasingly important. Safeguarding against intellectual property infringements and the subversion of AI-driven decision systems is a key area. Ongoing innovation addresses these security requirements.
Zero-Knowledge Proofs enable Large Language Models to operate with provable privacy and integrity, fostering trust in AI systems without exposing sensitive data.
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