1. Healthcare Sector: Data Collaboration and Privacy Protection
1. Cross-Institutional Joint Modeling
FHE allows medical institutions to conduct joint analyses without sharing original patient data (such as gene sequences and electronic medical records). For example, multiple hospitals can collaborate to train AI models using encrypted data for disease prediction or drug development, avoiding privacy leakage risks.
- AI Use Cases: Encrypted medical image analysis, personalized treatment plan generation, ensuring that AI model training data remains invisible throughout.
2. Biometric Verification
Biometric data such as fingerprints and irises of patients can be compared to the database in encrypted form for identity verification or medical insurance settlement, preventing the theft of biological information.
3. Clinical Trial Optimization
AI can simulate drug responses using encrypted patient historical data, accelerating clinical trial design while protecting subject privacy.
2. DeFi and Financial Sector: Balancing Privacy and Compliance
1. Privacy Transactions and MEV Protection
FHE can encrypt user transaction information (such as addresses and amounts), preventing MEV (Miner Extractable Value) attacks. For example, users submit encrypted transactions to the Mempool, and on-chain nodes process ciphertext data to complete settlements, eliminating front-running transactions.
- AI Use Cases: AI execution of encrypted trading strategies, such as hedge funds generating investment signals through FHE-protected models to prevent strategy leakage.
2. Compliance Regulation and Anti-Money Laundering
Regulatory agencies can perform compliance analysis on encrypted on-chain fund pools (such as identifying suspicious addresses) without obtaining users' plaintext transaction records, balancing privacy and regulatory needs.
3. DAO Governance Anonymization
Whale users can encrypt voting weights using FHE, allowing AI to statistically analyze encrypted data to generate governance outcomes, avoiding centralization and exposure of voting rights.
3. Gaming Sector: Fairness and Immersive Experience
1. Encrypted Game Logic
Gaming platforms can run core logic without obtaining players' plaintext data (such as card and equipment attributes). For example, in encrypted card battle games, AI judges determine outcomes through ciphertext calculations, eliminating the possibility of cheating.
- AI Use Cases: Dynamic difficulty adjustment algorithms operate on encrypted player behavior data to optimize gaming experience in real-time.
2. Asset Privacy in Blockchain Games
NFT attributes, rarity, and other data can be stored encrypted, and AI-driven market prediction models analyze supply and demand directly on ciphertext, protecting players' asset privacy.
4. Other Fields and General AI Applications
1. Advertising and User Behavior Analysis
AI models are trained on encrypted user behavior data to generate personalized advertising recommendations, avoiding platform abuse of raw data.
2. Gene Sequencing and Research Collaboration
Research institutions share encrypted genetic data through FHE, collaboratively training AI models to accelerate research on rare diseases while meeting ethical compliance.
3. Federated Learning Enhancement
Combining FHE with federated learning achieves 'dual privacy protection' during multi-party data collaboration for training AI, such as optimizing financial risk control models across institutions.
5. Technical Challenges and Breakthrough Directions
Despite the broad prospects of FHE, its implementation still needs to address:
1. Computational Overhead: Complex operations (such as logical judgments) require combining basic operations, leading to high computational costs, necessitating reliance on hardware acceleration (such as GPU/ASIC) for optimization.
2. Ecological Adaptation: Need to integrate with technologies like ZKP and TEE to construct a layered privacy framework, such as using ZKP to verify the correctness of FHE computations and reduce trust costs.
3. Standardization: Unifying cross-industry data formats and encryption protocols, such as establishing FHE encryption standards for medical data.
Summary
FHE provides a revolutionary solution for privacy computing in fields such as healthcare, DeFi, and gaming through its characteristic of 'data usable but not visible.' Its core value lies in balancing data value extraction with privacy protection, especially in highly regulated and sensitive scenarios where it is irreplaceable. With algorithm optimization and hardware performance improvements, the scaled application of FHE+AI is expected to become a key infrastructure for the integration of Web3 and Web2.