Ethereum: How do I implement a Merkle Tree?

Here is an article about implementing a Merkle tree:

Implementing a Merkle Tree in Ethereum

A Merkle tree is a data structure used for cryptographic hash functions, such as SHA-256. It allows you to efficiently verify the authenticity of data by creating a tree-like representation of hashes. In this article, we will see how to implement a Merkle tree in Ethereum.

What is a Merkle tree?

A Merkle tree is a binary tree where each node represents a hash value. The tree is built by recursively hashing the leaf nodes (individual blocks of data) and storing their hashes in the parent nodes. Each internal node contains the hash of its child nodes, making it a self-referential data structure.

Why do we need Merkle trees?

Merkle trees are essential for cryptographic protocols that rely on the secure sharing and transmission of data. For example, when encrypting data using public-key cryptography, the sender and receiver must agree on a shared secret key. To ensure the integrity of the encrypted data, the receiver can use the Merkle tree to verify the authenticity of the data.

How ​​to implement a Merkle tree in Ethereum?

In Ethereum, we use the Hashable trait to represent hash values. We create a MerkleNode structure that represents an internal node in the tree. Each MerkleNode contains two fields: the hash value of its child nodes and the hashes of its parent nodes.

use std::collections::HashMap;
// Define a Merkle Node Structure
struct MerkleNode {
hash: Hashable,
child_hashes: HashMap,
}
impl MerkleNode {
// Constructor to initialize a new MerkleNode
fn new(hash: Hashable) -> Self {
MerkleNode {
hash,
child_hashes: HashMap::new(),
}
}
// Method to calculate the hashes of the children
fn get_child_hashes(&self) -> &HashMap {
self.child_hashes.as_ref()
}
// Method to add a new child node to the tree
fn add_child_node(&mut self, hash: Hashable) {
self.hash = hash;
self.child_hashes.insert(hash, hash);
}
}

How ​​to build the Merkle tree

To build the Merkle tree in Ethereum, we use a recursive approach. We start with an empty MerkleRoot node, and then add each data block to the tree.

use std::collections::HashMap;
// Define a Merkle Root structure
struct MerkleRoot {
hashes: HashMap,
}
impl MerkleRoot {
// Constructor to initialize a new MerkleRoot
fn new() -> Self {
MerkleRoot { hashes: HashMap::new() }
}
// Method to add a data block to the tree
fn add_data_block(&mut self, hash: Hashable) {
self.hashes.insert(hash, hash);
}
// Method to build the Merkle tree recursively
fn build_tree(&self) -> Vec {
let mut tree = Vec::new();
for (hash, child_hashes) in self.hashes.iter() {
if *child_hashes.is_empty() {
tree.push(MerkleRoot::new());
} else {
tree.push(child_hashes.into_iter().next().unwrap().clone());
tree.extend(self.build_tree());
}
}
tree
}
}

Example use case

Here is an example of creating a Merkle tree for a data frame:

“`rust

use std::collections::HashMap;

// Define a data block

struct DataBlock {

id: usize,

}

impl DataBlock {

// Constructor to initialize a new DataBlock

fn new(id: usize) -> Self {

DataBlock { id }

}

// Method to add an additional field to the data block

fn add_field(&mut self, name: String, value: String) {

self.id += 1;

self.

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