Using Merkle Batch Proofs on Ergo

Comprehensive guide for implementing Merkle Batch Proofs in Ergo applications

Back to Merkle Batch Proofs

Overview

While full support for Merkle Trees and Batch Proofs is available in the sigma-rust library, which is extensively used in Rust-based Ergo applications, integration into the core JVM-based sigmastate-interpreter for direct use within ErgoScript remains in progress. There is an open issue in the sigmastate-interpreter repository to support Merkle Trees natively, which would allow for direct handling of static data within ErgoScript, enhancing efficiency in various blockchain operations, such as proof-of-transaction and securing extension block data.

Until this feature is fully integrated into sigmastate-interpreter, developers working in JVM-based environments can leverage scrypto, which provides essential cryptographic functionalities, including Merkle Tree management and Batch Proof generation and verification.

For more details on the ongoing integration efforts, you can refer to the GitHub issue #296 in the Ergo repository.

Now, let's delve into the practical steps of using Merkle Batch Proofs in Ergo.

Using Sigma-Rust for Rust-based Applications

The sigma-rust library provides comprehensive tools to create, manage, and verify Merkle Batch Proofs in Rust-based Ergo applications. Below is a step-by-step guide with code examples.

Step 1: Setting Up the Environment

Before you begin working with Merkle Batch Proofs in sigma-rust, ensure your environment is properly set up:

1. Clone the Sigma-Rust Repository

git clone https://github.com/ergoplatform/sigma-rust.git
cd sigma-rust

2. Install Rust

Follow the instructions on rust-lang.org to install Rust if you haven't done so.

3. Build the Project

cargo build

Step 2: Creating a Merkle Tree

First, create a Merkle tree using the MerkleTree structure provided by the sigma-rust library.

use sigma_merkle_tree::merkletree::MerkleTree;
use sigma_merkle_tree::MerkleNode;

fn create_merkle_tree() {
    let data_1 = [1u8; 32];
    let data_2 = [2u8; 32];
    let data_3 = [3u8; 32];

    let node_1 = MerkleNode::from_bytes(data_1);
    let node_2 = MerkleNode::from_bytes(data_2);
    let node_3 = MerkleNode::from_bytes(data_3);

    let tree = MerkleTree::new(vec![node_1, node_2, node_3]);
    println!("Merkle Root: {:?}", tree.root_hash());
}

Code Reference: The MerkleTree structure is implemented in the merkletree.rs file.

Step 3: Generating a Batch Merkle Proof

Once the Merkle tree is created, you can generate a batch Merkle proof for specific leaves. This proof can be used to verify the inclusion of multiple elements.

use sigma_merkle_tree::batchmerkleproof::BatchMerkleProof;

fn generate_batch_merkle_proof(tree: &MerkleTree) {
    let proof = tree.proof_by_indices(&[0, 2]).unwrap();
    println!("Batch Merkle Proof: {:?}", proof);
}

Code Reference: The BatchMerkleProof structure and its methods are implemented in the batchmerkleproof.rs file.

Step 4: Verifying the Batch Merkle Proof

The generated proof can be verified against the Merkle root to ensure that the specified leaves are indeed part of the tree.

fn verify_batch_merkle_proof(tree: &MerkleTree, proof: &BatchMerkleProof) {
    assert!(proof.valid(tree.root_hash().as_ref()));
    println!("Proof is valid.");
}

Step 5: Serialization and Deserialization

Merkle batch proofs can be serialized and deserialized for storage or transmission, which is crucial for many blockchain applications where proofs are shared between nodes or stored for future verification.

use sigma_ser::ScorexSerializable;

fn serialize_and_deserialize_proof(proof: &BatchMerkleProof) {
    let serialized_proof = proof.scorex_serialize_bytes().unwrap();
    let deserialized_proof = BatchMerkleProof::scorex_parse_bytes(&serialized_proof).unwrap();
    
    assert_eq!(proof, &deserialized_proof);
    println!("Serialization and deserialization successful.");
}

Using Scrypto for JVM-based Applications

For JVM-based applications, scrypto provides similar functionalities to sigma-rust. It works closely with the sigmastate-interpreter to handle cryptographic operations, including Merkle Trees and Batch Proofs.

Step 1: Setting Up the Environment

To work with scrypto, follow these steps:

1. Clone the Scrypto Repository

git clone https://github.com/input-output-hk/scrypto.git
cd scrypto

2. Ensure Scala and SBT Are Installed

You can install Scala and SBT by following the instructions on scala-lang.org.

3. Build the Project

sbt compile

Step 2: Creating a Merkle Tree

The Merkle Tree can be constructed using the MerkleTree class in Scrypto, similarly to how it's done in Rust.

import scorex.crypto.authds.merkle.MerkleTree
import scorex.crypto.authds.LeafData
import scorex.crypto.hash.Blake2b256

implicit val hf = Blake2b256

val leafData = Seq.fill(5)(LeafData @@ scorex.utils.Random.randomBytes(32))
val tree = MerkleTree(leafData)
println(s"Merkle Root: \${tree.rootHash}")

Code Reference: The MerkleTree class is implemented in the MerkleTree.scala file.

Step 3: Generating a Batch Merkle Proof

In Scrypto, you can generate a batch Merkle proof by specifying the indices of the leaves you want to prove.

import scorex.crypto.authds.merkle.{BatchMerkleProof, Leaf}

val batchProof = tree.proofByIndices(Seq(0, 2)).get
println(s"Batch Merkle Proof: \${batchProof}")

Code Reference: The BatchMerkleProof class and its methods are implemented in the BatchMerkleProof.scala file.

Step 4: Verifying the Batch Merkle Proof

You can then verify the generated proof to ensure that the elements are part of the Merkle tree.

val isValid = batchProof.valid(tree.rootHash)
println(s"Batch Merkle Proof is valid: \${isValid}")

Step 5: Serialization and Deserialization

To serialize and deserialize the proof, Scrypto provides dedicated classes and methods, ensuring the proof can be efficiently stored or transmitted.

import scorex.crypto.authds.merkle.serialization.BatchMerkleProofSerializer

val serializer = new BatchMerkleProofSerializer[Digest32, Blake2b256.type]
val serializedProof = serializer.serialize(batchProof)
val deserializedProof = serializer.deserialize(serializedProof).get

assert(batchProof == deserializedProof)
println("Serialization and deserialization successful.")

Code Reference: The BatchMerkleProofSerializer class is implemented in the BatchMerkleProofSerializer.scala file.

Example: End-to-End Workflow in Scrypto

Here's an example tying together all the steps in Scrypto:

import scorex.crypto.authds.merkle.{MerkleTree, Leaf, BatchMerkleProof}
import scorex.crypto.authds.merkle.serialization.BatchMerkleProofSerializer
import scorex.crypto.hash.Blake2b256
import scorex.crypto.hash.Digest32

implicit val hf = Blake2b256

// Create the Merkle tree
val leafData = Seq.fill(5)(LeafData @@ scorex.utils.Random.randomBytes(32))
val tree = MerkleTree(leafData)

// Generate the batch Merkle proof
val batchProof = tree.proofByIndices(Seq(0, 2)).get

// Verify the proof
val isValid = batchProof.valid(tree.rootHash)
println(s"Batch Merkle Proof is valid: \${isValid}")

// Serialize and deserialize the proof
val serializer = new BatchMerkleProofSerializer[Digest32, Blake2b256.type]
val serializedProof = serializer.serialize(batchProof)
val deserializedProof = serializer.deserialize(serializedProof).get

assert(batchProof == deserializedProof)
println("Serialization and deserialization successful.")

Implementation References

Sigma-Rust Libraries

Scrypto Libraries