1. Project Overview
Privasea is an innovative privacy computing network that integrates Fully Homomorphic Encryption (FHE), Artificial Intelligence (AI), and Decentralized Physical Infrastructure (DePIN). It aims to build a globally leading decentralized infrastructure for privacy-centric AI. By leveraging FHE, Privasea enables complex computations on encrypted data, preserving user privacy while unlocking data value. Compared to traditional methods such as cloud computing or federated learning, Privasea’s FHEML solution delivers true “computable but invisible” processing.
The Privasea network consists of four core components:
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HESea encryption library
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Developer-friendly API suite
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Distributed compute network "Privanetix"
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On-chain smart contract-based incentive system
These elements together create a sustainable decentralized AI ecosystem. Developers can easily integrate FHE-powered computation using Privasea’s APIs to deploy privacy-centric AI applications for both Web2 and Web3. All tasks are executed by distributed nodes, ensuring high availability, robust security, and seamless scalability.
At the application layer, Privasea launched
“ImHuman”, the first FHE-based Proof of Humanity solution, debuting on the
Solana ecosystem. It sets a new privacy-preserving standard for Sybil-resistance protocols. Users encrypt their facial vector data locally into NFTs, completing identity verification without uploading sensitive images—minimizing compliance risks and privacy leaks. It is a true “privacy-first” identity system.
Privasea is backed by Binance and OKX and has formed a deep partnership with privacy computing hardware manufacturer
Accseal, jointly exploring FHE + ZK hardware acceleration. The project is pioneering the industrialization of FHE + AI within the DePIN narrative, accelerating the arrival of the privacy-intelligent Web3 era.
2. Key Highlights
1. Real-world Application of FHE, Setting a New Bar for AI Privacy Privasea is one of the few projects to successfully integrate FHE into AI and machine learning. Its proprietary HESea encryption library supports mainstream schemes like TFHE and CKKS, enabling intensive computations on encrypted data. Compared to federated learning and ZK-based solutions, FHE offers stronger computational closure, ideal for data-sensitive industries such as finance and healthcare.
2. Decentralized Compute Network Privanetix – Driving DePIN from Narrative to Reality Privasea has built its own distributed compute layer, Privanetix, composed of nodes with real computational capabilities. These nodes execute FHE tasks and leverage on-chain smart contracts for task distribution, result verification, and reward settlement—forming a closed-loop compute resource scheduling under the DePIN framework. This significantly boosts system throughput and resilience.
3. ImHuman Deploys on Solana – A New Paradigm in Sybil Resistance ImHuman, Privasea’s flagship application, is live on Solana mainnet and is the first decentralized identity verification system powered by FHE. Users create encrypted facial vector NFTs locally, with no need to upload images, thus preserving privacy while preventing Sybil attacks. Compared to projects like Worldcoin, ImHuman offers stronger compliance and privacy protections.
4. Strategic Partnership with Accseal – Exploring Hardware Acceleration for FHE + ZK To boost system performance, Privasea has partnered with Accseal to develop ASIC-based cryptographic accelerators for FHE workloads. The project is also exploring integration with ZK technologies to build a multilayered security framework serving AI, identity, and data analytics—paving the way toward enterprise-grade encryption infrastructure.
5. $PRVA
Token
Utility Anchored in Computation and Services The native token $PRVA powers multiple core functions such as node incentives, API billing, and governance. Nodes are rewarded in $PRVA for completing FHE computations, and developers pay in $PRVA to access API services. This usage-based token model creates an internal value loop and provides long-term valuation support.
3. Market Outlook
$PRAI is the native token of the Privasea network, supporting critical roles in FHE + AI + DePIN architecture—ranging from computation incentives and API usage to identity verification. It represents a new class of “utility-driven privacy compute assets.” Amid growing interest in crypto identity, ZK, and FHE narratives, $PRAI enjoys both high-frequency use cases and potential industry premium.
Currently, $PRAI is priced at $0.08262 with a circulating market cap of approximately $16.98 million—undervalued compared to mainstream AI narrative tokens. Relative to other FHE-focused infrastructure projects, or AI/ML and data factory-oriented players like Wayfinder or Virtuals Protocol, Privasea demonstrates tangible progress across application deployment, token utility, and real node activity—suggesting ample room for revaluation.
If ImHuman continues gaining traction within Solana’s airdrop verification ecosystem and the FHE + ZK hardware acceleration module is successfully deployed, $PRAI—as the “computational fuel for Confidential AI on-chain”—could undergo stepwise market cap growth.
4. Tokenomics
Total Supply: 1,000,000,000 $PRAI
Distribution:
Mining 1: 30% – Incentives for early nodes participating in FHE computation and AI tasks.
Investors: 13.45% – Allocated to institutional investors with structured vesting to limit sell pressure.
Marketing Community 1: 12.97% – Used for brand building, user growth, developer onboarding, airdrops.
Reserve: 10.05% – Strategic reserves for future development and partnerships.
Early Contributors: 9.04% – Allocated to early contributors, advisors, and technical supporters.
Team: 8% – Linear vesting to incentivize long-term development.
Mining 2: 5% – Reserved for later-stage compute node activation.
Liquidity: 4% – DEX and IDO liquidity provisioning.
Future Airdrop: 3% – Reserved for ecosystem growth and user onboarding.
Binance IDO Wallet: 2% – For Binance IDO participation and related rewards.
Marketing Community 2: 2% – For global expansion and offline activities.
Strategic: 0.5% – Reserved for major partnerships (enterprise, government, protocol-level).
Key Utilities:
Access Confidential AI Services – Pay for FHE inference, model calls, and data analysis on DeepSea AI Network.
Compute Fuel Node Rewards – Used as gas to execute encrypted computations and reward participating nodes.
Proof of Humanity Verification in ImHuman – Pay for multi-factor biometric verification (face, voice, fingerprint).
AI Agent Activation Customization – Activate intelligent agents for data analysis, automation, and social mining.
Staking Network Security – Stake PRAI to support network operation and enhance security.
Model Marketplace Developer Ecosystem – Developers monetize AI models; enterprises access services via PRAI.
Governance – PRAI holders vote on core proposals such as parameter changes, model launches, and treasury use.
5. Team Fundraising
Team Privasea was founded by AI and privacy tech expert
David Jiao, with
Martin Tang and
Gigidrgn serving as co-founders and business leads. The team has a strong background in FHE, AI compute, and Web3 development. Core members are based in Switzerland and across Europe, with experience in crypto engineering, tokenomics, and DePIN infrastructure. Since its inception in 2022, the project has focused on decentralized privacy compute, AI inference, encrypted identity verification, and scalable compute networks.
Fundraising
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Seed Round (March 2024): $5M – YZi Labs, MH Ventures, Gate Labs, OxBull, Danu Ventures, Crypto Times
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Strategic Round (April 2024): Undisclosed – OKX Ventures, Laser Digital, Tané
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Pre-A Round (September 2024): Undisclosed – Oasis Labs
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Series A (January 2024): Valuation $180M – GSR, Amber Group
6. Risk Factors
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Limited Real-World Demand in Early Stages – Despite clear token utility in computation and governance, actual user payment scenarios are still emerging. Without ecosystem flywheel effects, price volatility may persist.
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FHE Technical Challenges – While FHE provides unmatched privacy protection theoretically, practical issues like performance bottlenecks and development complexity remain. Without continued optimization, real-world deployment may stall.
7. Official Links
