Merkle Tree
Table of Contents
- used to efficiently verify and summarize large amounts of data.
- like a pyramid of hashes, where each level summarizes the information below it.
1. Working mechanism
- Data Blocks: The bottom level of the tree consists of hashes of individual data blocks (e.g., transactions, files).
- Parent Nodes: Each pair of sibling nodes (hashes) is combined and hashed to create a parent node.
- Root Hash: This process continues upwards, creating new parent nodes, until there's only one node left at the top – the Merkle root.
2. Key Properties
- Tamper-Proof: If even a single bit of data changes in a block, the corresponding hash will change, propagating the change up the tree to the root. This makes it easy to detect any modifications.
- Efficient Verification: You can verify if a specific data block is part of a larger dataset by only checking a small number of hashes in the Merkle tree path leading to the root, rather than the entire dataset.
- Data Integrity: The Merkle root acts as a unique fingerprint for the entire dataset. Any change to the data alters the root hash.
3. Uses
- Blockchain: Merkle trees are used in blockchains to summarize all transactions in a block and ensure their integrity.
- Peer-to-Peer Networks: They help verify the consistency of files shared across different peers.
- Distributed Systems: They are used to detect inconsistencies between replicas of data.
4. Key Benefits
- Efficient Verification: Makes verifying large datasets quick and easy.
- Data Integrity: Ensures data remains unaltered and secure.
- Tamper Detection: Quickly detects any changes or inconsistencies in the data.
5. Example
Given a Merkle tree with four data blocks (A, B, C, D):
Root Hash
/ \
Hash AB Hash CD
/ \ / \
Hash A Hash B Hash C Hash D
If you want to verify that block C is in the original dataset, you only need to check Hash C, Hash CD, and the Root Hash, instead of checking all four blocks.