Aleo: An Innovative Blockchain Project for Privacy Protection and Scalability

Aleo is a blockchain project dedicated to privacy protection, achieving higher privacy and scalability through zero-knowledge proof technology (ZKP). The core idea of the project is to enable users to authenticate their identity and process data without disclosing personal information.

Project Overview

Privacy

The core technology of Aleo is zero-knowledge proofs (ZKPs), which allows transactions and the execution of smart contracts to occur while maintaining privacy. User transaction details, such as the sender and transaction amount, are hidden by default. This design not only protects user privacy but also enables selective disclosure when necessary, making it very suitable for the development of decentralized finance (DeFi) applications.

The main components of Aleo include:

1. Leo Compiled Language: Adapted from the Rust language, specifically designed for developing zero-knowledge applications (ZKApps), reducing the requirements for cryptographic knowledge for developers.

2. snarkVM and snarkOS: snarkVM allows for off-chain execution of computations, with only the verification of the computation results on-chain, thereby improving efficiency. snarkOS ensures the security of data and computations while allowing permissionless functionality execution.

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

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

Scalability

Aleo adopts an off-chain processing method, where transactions are first computed for proof on the user's device, and only the verification results are uploaded to the Blockchain. This method greatly enhances the transaction processing speed and the system's scalability, avoiding issues such as network congestion and high fees similar to Ethereum.

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 enhances the degree of decentralization of the network but also improves performance and security.

1. Block quick finality: AleoBFT ensures that each block is immediately confirmed after generation, enhancing 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 to prevent a few entities from monopolizing the network.

3. Incentive Mechanism: Validators and Provers share Block rewards; Provers are encouraged to become Validators by staking tokens, thereby enhancing the decentralization and computational power of the network.

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

Latest Developments

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

1. ARC-100 Proposal Passed: This proposal involves compliance aspects, security measures such as the locking of funds on the Aleo network and delayed transactions, and the team is currently making final adjustments.

2. Validator Incentive Program: This program will launch on July 1st and aims to validate the new puzzle mechanism. The program will run until July 15th, during which 1 million Aleo points will be allocated as 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 task rewards (75 million) and will be distributed within the first six months, including rewards for staking, running validators, and validating nodes.

4. Testnet Update: The last network reset will complete the addition of ARC-41 and the new puzzle feature.

5. Code freeze: Completed a 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, while to become a validator, 10 million tokens are needed. These amounts will gradually decrease over time.

Synthesis Puzzle Algorithm Analysis

The latest version of Aleo's puzzle algorithm is called Synthesis Puzzle, and its main features are as follows:

1. A common EpochProgram is generated for each epoch.

2. Build the R1CS proof circuit using the input and EpochProgram to generate the R1CS assignment (witness).

3. Use the witness as a leaf node of the Merkle tree, calculate the Merkle root and convert it to the proof_target of the solution.

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

5. The EpochProgram generates random numbers sampled from the instruction set based on the current epoch_hash.

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

7. After calculating the witness, convert it to a sequence of leaf nodes of the Merkle tree, using an 8-ary Merkle tree with a depth of 8.

8. Calculate the Merkle root and convert it to the solution's proof_target, checking if it meets the latest_proof_target of the current epoch.

9. The input of the EpochProgram can be updated through the iterative counter in the same epoch to perform multiple solution calculations.

This update changes the 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. Compared to the previous testnet, the new algorithm discards MSM and NTT calculations, and the implementation of parallelization faces new challenges. This change may have a significant impact on existing mining optimization strategies, requiring miners to readjust and adapt to the new algorithm characteristics.