Aleo: A Pioneer of Privacy Protection and Scalability

Aleo is a blockchain project focused on privacy protection, achieving higher privacy and scalability through zero-knowledge proof technology (ZKP). The core idea of the project is to enable users to conduct identity verification and data processing without disclosing personal data.

Project Overview

Privacy

At the core of Aleo is zero-knowledge proof (ZKPs) technology, which allows transactions and the execution of smart contracts to be carried out while maintaining privacy. The details of users' transactions are hidden by default, while allowing for selective disclosure when necessary, making it ideal for the development of DeFi applications. The main components include:

1. Leo Programming Language: Adapted from Rust, specifically designed for developing zero-knowledge applications (ZKApps), reducing the requirements for developers' knowledge of cryptography.

2. snarkVM and snarkOS: snarkVM allows off-chain computation while only verifying the results on-chain, improving efficiency. snarkOS ensures the security of data and computation, and allows permissionless execution of functions.

3. zkCloud: Provides a secure and private off-chain computing environment that supports programming interactions between users, organizations, and DAOs.

Aleo also provides an integrated development environment (IDE) and software development kit (SDK), supporting developers to quickly write and publish applications. Developers can deploy applications in Aleo's program registry without relying on third parties, reducing platform risk.

Scalability

Aleo adopts an off-chain processing method, where transactions are first computed on user devices to generate proofs, and then only the verification results are uploaded to the blockchain. This method greatly improves transaction processing speed and system scalability, avoiding network congestion and high fees.

consensus mechanism

Aleo has introduced AleoBFT, a consensus mechanism with a hybrid architecture that combines the instant finality of validators with the computational power of provers. AleoBFT not only improves the degree of decentralization of the network but also enhances its performance and security.

1. Fast Block Finality: AleoBFT ensures that each block is confirmed immediately after it is generated, improving node stability and user experience.

2. Decentralized Assurance: By separating block production from coinbase generation, validators are responsible for generating blocks while provers perform proof calculations, preventing a few entities from monopolizing the network.

3. Incentive mechanism: Validators and verifiers share block rewards; encourages verifiers to become validators by staking tokens, enhancing the decentralization and computational power of the network.

Aleo allows developers to create applications that are not subject to gas limits, making it particularly suitable for applications that require long-running processes, such as machine learning.

Latest Developments

Aleo will launch its incentive testnet on July 1st, here are some important updates:

1. ARC-100 voting approved: The proposal involving compliance aspects, locking of funds online, and security measures such as delayed deposits has been approved, and the team is making final adjustments.

2. Validator Incentive Program: Running from July 1 to 15, distributing 1 million Aleo points as rewards. The percentage of points generated by the node will determine its share of the rewards, and each validator must earn at least 100 tokens to be eligible for rewards.

3. Token Supply: The initial supply is 1.5 billion tokens, with an initial circulating supply of approximately 10%. These tokens primarily come from Coinbase tasks (75 million) and will be distributed over the first six months, which also includes rewards for staking, running validators, and validating nodes.

4. Testnet Beta Reset: This is the final network reset, and no new features will be added after completion; the network will be similar to the mainnet.

5. Code freeze: Completed one week ago.

6. Validator Node Expansion Plan: The initial number of validator nodes is 15, with a target to increase to 50 within the year, and ultimately reach 500. To become a delegator, 10,000 tokens are required, and to become a validator, 10 million tokens are needed. These amounts will gradually decrease over time.

Synthesis Puzzle Algorithm Analysis

The latest algorithm core of Aleo is called Synthesis Puzzle, which is centered around the fixed generation of a common EpochProgram for each epoch. By constructing R1CS proof circuits for the inputs and the EpochProgram, corresponding R1CS assignments (i.e., witnesses) are produced and serve as the leaf nodes of a Merkle tree. After calculating all the leaf nodes, the Merkle root is generated and converted into the proof_target of the solution.

The detailed process of building a Synthesis Puzzle is as follows:

1. Each puzzle calculation is called a nonce, constructed from the address receiving the mining reward, epoch_hash, and the random number counter.

2. In each epoch, the EpochProgram computed by all provers is the same, sampled from the instruction set using the random number generated by the current epoch_hash.

3. Use nonce as a random seed to generate the input for EpochProgram.

4. Aggregate the R1CS and input corresponding to the EpochProgram to perform witness calculation.

5. After calculating all the witnesses, convert them into a sequence of leaf nodes for the Merkle tree.

6. Calculate the Merkle root and convert it to the solution's proof_target, and determine if it meets the current epoch's latest_proof_target.

7. Multiple solution calculations can be performed by updating the input of the EpochProgram through the iterative counter within the same epoch.

This update changes puzzle from generating proof to generating witness. The calculation logic for all solutions within each epoch is consistent, but there are significant differences in the calculation logic between different epochs. The new algorithm discards the previous MSM and NTT calculations, and due to the serial execution dependencies in the witness generation process, achieving parallelization presents certain challenges.