What is Privasea AI (PRAI)?

Deep Dive

1. Core Purpose & Technology

Privasea addresses the conflict between AI utility and data privacy via Fully Homomorphic Encryption (FHE), which allows computations on encrypted data. This ensures sensitive information (e.g., medical records, biometrics) remains private during AI model training or inference. The network comprises:
- DeepSea AI Network: A decentralized layer for encrypted AI tasks, powered by nodes that execute FHE-based computations (Privasea Blog).
- FheID: A mobile app that verifies human identity using encrypted face/palm scans, generating reusable Proof-of-Humanity credentials without storing raw data (Privasea Tweet).

2. Tokenomics & Ecosystem

PRAI’s 1 billion tokens drive utility across:
- Network Access: Pay for encrypted AI services (e.g., healthcare analytics, fraud detection).
- Governance: Vote on upgrades, AI model releases, and funding decisions.
- Staking: Secure the network and earn rewards by operating nodes (Tokenomics Blog).
Developers monetize AI models on DeepSea, while enterprises use PRAI to integrate privacy-compliant solutions (e.g., GDPR).

3. Key Differentiators

Unlike conventional privacy tools (e.g., zero-knowledge proofs), Privasea’s FHE framework supports complex AI computations on encrypted data. Partnerships with telecom firms for FHE-based eSIMs and collaborations with projects like Zama highlight its focus on scalable, real-world adoption (Blynex Academy).

Conclusion

Privasea AI redefines data privacy in AI by merging FHE with decentralized infrastructure, positioning PRAI as both a utility token and governance mechanism. As industries demand compliant AI, can Privasea’s encrypted compute layer become the standard for sensitive data processing?