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Ethereum's need to scale has led to the development of Layer 2 solutions, with ZK/OP rollups emerging as key players, forming a short-term OP and long-term ZK consensus, highlighting ARB, OP, zkSync, and StarkNet as major contenders. Web3 users prioritize privacy only when it provides economic value. FHE's encryption cost further burdens the already low on-chain efficiency, and large-scale adoption is feasible only when significant benefits justify the cost. For institutional clients needing public blockchains but unwilling to disclose all information, FHE's ability to display and trade ciphertext is more suitable than ZKP.

The application scenarios of FHE are vast, extending beyond just Web3 and blockchain. It caters to any private data within the entire internet ecosystem. This article will introduce you to the main participants and application scenarios of the FHE track.

Fully Homomorphic Encryption (FHE) represents the cutting edge of privacy protection technology. It offers exceptional privacy safeguards and can be utilized in Web3 for securing transaction privacy, protecting AI data, and enhancing privacy in co-processing units.

FHE is a novel encryption technology that addresses the limitations of zero-knowledge proofs in privacy protection and scalability. It allows for sharing and protecting private states without the need for third-party trust, and enables direct computation on encrypted data, supporting various applications.

Bitcoin and Ethereum have eliminated intermediaries in financial transactions but sacrificed privacy. With the development of zero-knowledge proof technology, on-chain privacy has become a core theme of Web 3. Aztec and Aleo are two promising networks. ZKP is suitable for private state changes, protecting user privacy, and can be used for use cases such as anonymous social media and enterprise invoices/payments. FHE methods can address the issue of sharing private states, applicable to scenarios like uncollateralized DeFi loans and on-chain KYC. MPC methods can protect the privacy of private keys and data, suitable for decentralized AI training and inference. These technologies can be combined to achieve a more comprehensive protection effect.

Gate Research Daily Report (October 29): In the past 24 hours, BTC has increased by 2.84%, approaching the $72,000 resistance level. The Total Value Locked (TVL) on Solana’s decentralized exchange Jupiter has reached a new high, and the market capitalization of Liquid Staking Tokens (LSTs) has exceeded $5 billion. Base protocol fees have surpassed $23 million. Phantom wallet experienced a temporary outage due to the Grass airdrop, and Circle has released a privacy framework for ERC-20 tokens. Additionally, Elon Musk’s PAC activities are now facing a lawsuit.

FHE (Fully Homomorphic Encryption) allows third parties to perform unlimited calculations and operations on encrypted data without decryption, thus achieving combinable on-chain privacy calculations. ArkStream Capital has written an article introducing the concept, application scenarios, and ecosystem of FHE, as well as the FHE-Rollup type Layer2 solution that Fhenix is building.

Ethereum's privacy issues are increasingly gaining attention, especially as transaction transparency may expose users' financial information and activities. To address this issue, Stealth Addresses have been proposed, aiming to ensure the receiver's identity and transaction details remain private by generating a unique temporary address for each transaction. This method does not rely on third-party privacy protocols but enhances privacy directly at the protocol level. However, the implementation of Stealth Addresses still faces challenges.

AI+Crypto is one of the most notable frontier areas in the recent cryptocurrency market. This includes decentralized AI training, GPU DePINs, and censorship-resistant AI models. The main issue with AI training lies in the need for high-speed communication and coordination between GPUs, as neural networks require backpropagation during training. Introducing a decentralized network can significantly slow down the process due to increased network latency and bandwidth. This article also organizes solutions to the current challenges and provides examples of how the integration of cryptocurrency and AI can add significant value.

Equilibrium Research has released its annual prediction report, outlining potential events and industry trends expected by the end of next year. The report covers areas such as Scaling, ZK Proving, Privacy, Consensus & P2P Networking, and User Experience.