Secure inference is the process of performing computations with an artificial intelligence model on sensitive data without revealing the data or the model itself. This is often accomplished using advanced cryptographic methods such as homomorphic encryption or secure multi-party computation. It permits confidential data processing while preserving user privacy. This technology is vital for sensitive AI applications.
Context
Secure inference holds considerable importance for privacy-preserving AI applications in fields like decentralized finance and blockchain analytics. Processing confidential information with AI models necessitates robust privacy safeguards. Progress in this domain is essential for expanding AI utility in regulated environments.
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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