Aleo: A Privacy-Preserving Blockchain Powered by zk-SNARKs Technology

Aleo is a blockchain project focused on privacy protection, achieving a higher level of privacy and scalability through zk-SNARKs technology. The core idea of the project is to enable users to perform identity verification and data processing without disclosing personal information.

Project Overview

Privacy

The core of Aleo is zk-SNARKs ( ZKPs ) technology, which allows transactions and smart contracts to be executed while protecting privacy. The details of users' transactions are hidden by default, but selective disclosure is allowed when necessary, a feature that is very suitable for the development of DeFi applications. Its main components include:

1. Leo programming language: A language specifically designed for developing zero-knowledge applications ( ZKApps ), which reduces the requirements for cryptography knowledge for developers.

2. snarkVM and snarkOS: the former allows off-chain execution of computations, while the latter ensures the security of data and computations.

3. zkCloud: Provides a secure and private off-chain computing environment.

Aleo also provides an integrated development environment and software development kit, supporting developers to quickly write and publish applications.

Scalability

Aleo adopts an off-chain processing method, where transactions are first computed on the user's device to generate a proof, and then only the verification result is uploaded to the Blockchain. This method greatly improves transaction processing speed and system scalability, avoiding network congestion and high fee issues.

Consensus Mechanism

Aleo introduced AleoBFT, a hybrid architecture consensus mechanism 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: ensure that each block is confirmed immediately after it is generated.

2. Decentralized Assurance: By separating block production from coinbase generation, it prevents a few entities from monopolizing the network.

3. Incentive Mechanism: Validators and Provers share Block rewards, encouraging Provers to become Validators by staking tokens.

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

Latest Developments

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

1. ARC-100 vote passed: The proposal involving compliance aspects, the locking of funds on the network, and security measures such as delayed transactions has been approved.

2. Validator Incentive Program: It will launch on July 1st, aimed at validating 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%.

4. Testnet Beta Reset: This is the final network reset, and no new features will be added after it is completed.

5. Code freeze: Completed one week ago.

6. Validation Node Expansion Plan: The initial number of validation nodes is 15, with a target to increase to 50 within the year, and ultimately reach 500.

Synthesis Puzzle Algorithm Analysis

The latest version of Aleo's puzzle algorithm is called Synthesis Puzzle, which is centered around generating a common EpochProgram fixed for each epoch. By constructing R1CS proof circuits for the inputs and the EpochProgram, it produces corresponding R1CS assignments that serve as the leaf nodes of a Merkle tree. After calculating all the leaf nodes, it generates the Merkle root and converts it into the proof_target of the solution.

algorithm process

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 Epoch Program calculated 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 number seed to generate the input for EpochProgram.

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

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

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

7. In the same epoch, multiple solution calculations can be performed by iteratively updating the input of EpochProgram through the counter.

Mining Changes and Impacts

The new algorithm 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. This change abandons the previous optimizations for MSM and NTT calculations, and due to the serial execution dependencies in the witness generation process, achieving parallelization faces significant challenges.