The Core Breakthrough of FHE: Free Computation on Encrypted Data

FHE allows direct computation on encrypted data (such as addition, multiplication, neural network inference) without decryption. This feature addresses two major pain points of traditional AI:

Data Privacy Paradox: Sensitive data in fields like healthcare and finance cannot be directly used for model training due to compliance requirements, leading to limitations in AI development.

Centralized Risk: User data must be uploaded to the cloud or third-party platforms, posing risks of leakage and misuse.

The innovation of MindNetwork may lie in building a decentralized FHE computation network, processing encrypted data through collaborative distributed nodes, further reducing single point risks.

Future Outlook: AI New Paradigm Driven by FHE

Short-term (1-3 years): Vertical fields such as financial risk control and medical image analysis will be the first to implement, with the emergence of FHE acceleration chips.

Mid-term (5 years): Integration with Zero-Knowledge Proofs (ZKP) and Secure Multi-Party Computation (MPC) to form a hybrid privacy computing protocol.

Long-term (10 years): FHE becomes the default option for AI infrastructure, promoting the arrival of the 'Privacy-Native' AI era.

#MindNetwork Homomorphic Encryption FHE Reshaping the Future of AI @mindnetwork_xyz