What is the Aleo Blockchain Privacy Project? The latest comprehensive interpretation of Aleo! Aleo is a blockchain project focused on privacy protection, achieving higher privacy and scalability through zero-knowledge proof technology (ZKP). The core idea of Aleo is to enable users to authenticate and process data without revealing their personal data. So what is the Aleo blockchain privacy protection project? What are the characteristics of the Aleo blockchain privacy protection project? What is the current progress of the Aleo blockchain privacy protection project?
Today, the editor of this website introduced to you the project outline and latest progress of Aleo, and gave a detailed explanation of the puzzle algorithm update that the market is very concerned about. Friends who need it can take a look together!
Aleo network randomly generates a ZK circuit every hour; miners need to try different nonce as the input of the circuit during this hour, and calculate the witness (that is, all variables in the circuit, this calculation The process is also called synthesisize). After obtaining the Merkle root for the witness, it is judged whether it meets the mining difficulty requirements. Due to the randomness of the circuit, this mining algorithm is not friendly to GPUs and has great difficulty in computing acceleration.
Aleo completed a US$28 million Series A financing led by a16z in 2021, and a US$200 million Series B financing in 2024. Investors include Kora Management, SoftBank Vision Fund 2, Tiger Global, Sea Capital, Slow Ventures and Samsung Next, among others. The funding round values Aleo at $1.45 billion.
The core of Aleo is zero-knowledge proofs (ZKPs) technology, which allows transactions and smart contract execution to be performed while maintaining privacy. User's transaction details, such as sender and transaction amount, are hidden by default. This design not only protects user privacy, but also allows selective disclosure when necessary, which is very suitable for the development of DeFi applications. Its main components include:
Aleo also provides an integrated development environment (IDE) and software development kit (SDK) to support developers to quickly write and publish applications; in addition, developers can deploy applications in Aleo's program registry without relying on third parties , thus reducing platform risks.
Aleo adopts an off-chain processing method. The transaction is first calculated on the user device to prove it, and then only the verification result is uploaded to the blockchain. This method greatly improves the processing speed of transactions and the scalability of the system, and avoids network congestion and high fees similar to Ethereum.
Aleo introduces AleoBFT, a hybrid architecture consensus mechanism that combines the instant finality of validators and the computing power of provers. AleoBFT not only increases the decentralization of the network, but also enhances performance and security.
Aleo allows developers to create applications that are not gas restricted, making it especially suitable for long-running applications such as machine learning.
Aleo will launch the incentivized testnet on July 1st, the following is some important latest information:
While announcing the latest test network news recently, Aleo has updated the latest version of the puzzle algorithm. The new algorithm no longer focuses on the generation of zk proof results, and has removed MSM and NTT (2 The calculation module is used extensively to generate proof in zk. Previously, testnet participants used calculations to optimize the efficiency of the algorithm to increase mining revenue, focusing on the generation of intermediate data witness before generating proof. We will give a brief introduction to the latest algorithm after referring to the official puzzle spec and code.
At the consensus protocol level, the prover and validator in the process are respectively responsible for generating the calculation result solution and generating the block, aggregating and packaging the solution. The process is as follows:
The latest version The core of the algorithm is called Synthesis Puzzle. Its core is to generate a common EpochProgram for each epoch. By constructing an R1CS proof circuit for the input and EpochProgram, the corresponding R1CS assignment (that is, the witness mentioned by everyone) is generated and used as a leaf node of the Merkle tree. , after calculating all leaf nodes, the Merkle root is generated and converted into the proof_target of the solution. The detailed process and specifications for building Synthesis Puzzle are as follows:
1. Each puzzle calculation is called nonce, which is constructed from the address that receives the mining reward, epoch_hash and a random number counter. Each time you need to calculate a new solution, you can pass Update the counter to obtain a new nonce
2. In each epoch, the EpochProgram that all provers in the network need to calculate is the same. It is sampled from the instruction set by the random number generated by the current epoch_hash. The sampling logic is:
3. Use nonce as a random number seed to generate the input of the EpochProgram. 4. Aggregate the R1CS and input corresponding to the EpochProgram. witness (R1CS assignment) calculates
5. After calculating all witnesses, these witnesses will be converted into the leaf node sequence of the corresponding merkle tree. The merkle tree is an 8-element K-ary Merkle tree with a depth of 8
6. Calculate the merkle root and convert it into the proof_target of the solution, and determine whether it meets the latest_proof_target of the current epoch. If it is satisfied, the calculation is successful. Submit the reward address, epoch_hash and counter required to build the input above as the solution and broadcast
7. The same In epoch, the input of EpochProgram can be updated by iterating counter to perform multiple solution calculations
After this update, the puzzle has changed from generating proof to generating witness. The calculation logic of all solutions in each epoch is the same, but the calculation logic of different epochs is quite different.
From the previous test network, we can find that many optimization methods focus on using GPU to optimize the MSM and NTT calculations in the proof generation stage to improve mining efficiency. This update completely abandons this part of the calculation; at the same time, due to the generation of witness The process is generated by executing a program that changes with epochs, and the instructions therein will have some dependencies on serial execution, so achieving parallelization is a big challenge.
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