As a part-time DJ at a major Web3 company, would you go to see it?

Just joking, the recent market has been volatile, many projects are inactive, VCs are losing big, and retail investors are just lying flat!

Not many have performed well in the downturn; @Mind Network is one of the few.

FHE (Fully Homomorphic Encryption), as the 'holy grail' technology of privacy computing, allows for direct computation on encrypted data without exposing plaintext, and is becoming a key tool for breaking through the privacy and efficiency dilemma in fields like AI, healthcare, DeFi, and gaming.

1. Transformative Use Cases in the AI Field

AI model training relies on massive amounts of data, but the privacy issues of sensitive data (such as medical records and financial information) have long hindered its development. FHE allows institutions to directly train models on encrypted data, for example:

Collaborative Modeling: Multiple hospitals can share encrypted genomic data through FHE to collaboratively train disease prediction models without disclosing patient privacy.

Trusted Inference: Users input encrypted financial data into an AI model, and the model returns an encrypted result that only the user can decrypt, avoiding third-party misuse of data.

Mind Network provides a decentralized privacy computing framework for AI Agents through FHE Chain, supporting multi-party collaboration on encrypted data and ensuring that the inference process is transparent and verifiable.

Multi-Agent Secure Collaboration

2. Privacy Breakthroughs in the Healthcare Field

Electronic Health Records (EHR): Hospitals can query and statistically analyze encrypted patient data, supporting disease trend research while avoiding plaintext exposure.

Medical Image Processing: Radiologists can directly enhance or diagnose encrypted CT/MRI images, with the original data visible only to authorized parties.

Genetics and Personalized Medicine

Supports whole genome association analysis on encrypted genomic data #MindNetwork全同态加密FHE重塑AI未来