Merkle tree packages

Merkle Tree Package overview

This page explores a few different Merkle tree implementations available in Python.

pymerkle

https://pymerkle.readthedocs.io/en/latest/

from pymerkle import MerkleTree
mt = MerkleTree()

# encrypt an entire file
mt.encryptFileContent(path_to_file)

# text based representation of the tree
print(mt)

# create a challenge using our computed checksum
challenge = {
    'checksum': hex_string
}
proof = mt.merkleProof(challenge)

# Create a validator to check if a proof exists
validator = Validator(proof)
try:
    validator.run()
    print('Valid proof')
except merkle_exceptions.InvalidMerkleProof as e:
    print(f'Invalid proof: {e}')

# import/export
mt.export(path_to_export)
mt = MerkleTree.loadFromFile(path_to_import)

Pros:

• Directly encrypt files or logs entries within a file
• Easy to import/export
• Tree is automatically expanded when new nodes are added
• Text visualization
• Good documentation

Cons:

• TODO figure out how to compute hash properly
• Some security concerns exist https://github.com/fmerg/pymerkle/tree/master#security

merkletools

https://github.com/Tierion/pymerkletools

from merkletools import MerkleTools
mt = MerkleTools()
mt.add_leaf(json_data, True)  # add all the data to the tree, each item in json data becomes a leaf
mt.make_tree()  # create the tree itself, adding more nodes will reset the tree (i.e. we will need to run this command again)

# grab various metrics from the tree
root = mt.get_merkle_root()
proof_0 = mt.get_proof(0)
leaf_0 = mt.get_leaf(0)

# manaually compute the hash of one of the elements
hash = hashlib.sha256(json_data[0].encode()).digest()
hex_string = hash.hex()

# validate the hash is present in the data
is_valid = mt.validate_proof(proof_0, hex_string, root)
print(f'Validating leaf 1: {is_valid}')
print(f'Leaf hex == manual hash: {leaf_0 == hex_string}')

Pros:

• Easily able to validate hash and confirm it exists or not in the tree
• Easy to add leave

Cons:

• No visual representation of tree
• Required to run mt.make_tree() whenever new nodes are added
• No straightforward way to import/export, but the pickle package exists and can probably handle this
• Had to read entire contents of file and then encrypt

merklelib

https://github.com/vpaliy/merklelib

from merklelib import MerkleTree, beautify

# our own defined hash function, could easily sprinkle some salt in here
def hashfunc(value):
    return hashlib.sha256(value).hexdigest()

mt = MerkleTree(data, hashfunc)

beautify(mt)  # text based visualization of the tree

leaf_0 = mt.hexleaves[0]
proof_0 = mt.get_proof(leaf_0)

# check if leaf is in tree or not based on proof
if mt.verify_leaf_inclusion(mt.leaves[0], proof_0):
    print('In tree')
else:
    print('Not in tree')

# export Merkle tree, json or jpg
export(tree, filename='transactions', ext='jpg')

Pros:

• Easy to export, both JSON or JPEG supported
• Able to define our own hash function, easy to include salt
• Text based visualization
• Proofs are straightforward

Cons:

• All data is passed into constructor instead of creating the tree and then adding. Include update, append, and extend method but does not state what they do.
• Lack of documentation