Merkle Tree

• Merkle Tree
  • 1. What is a Merkle Tree?
    • How It Works
  • 2. Key Features
  • 3. Scenarios \& Applications
    • 1. Version Control Systems (e.g., Git)
    • 2. File Synchronization (e.g., Dropbox, rsync)
    • 3. Blockchain \& Distributed Systems
    • 4. Backup Systems
    • 5. Peer-to-Peer Networks (e.g., BitTorrent)
    • 6. Anti-Virus Scanners
  • 4. Example Workflow
  • 5. Limitations
  • Alternatives

Also called a hash tree.

It is designed to efficiently verify the integrity and consistency of data structures, such as file systems, by using cryptographic hashes (like CRC, though Merkle Trees typically use stronger hashes like SHA-256).

1. What is a Merkle Tree?

A Merkle Tree is a hierarchical structure where:

• Leaf nodes represent the hashes of individual data blocks (e.g., files, using their CRC).
• Non-leaf nodes represent the hash of their child nodes’ combined hashes.
• The root node (top-level hash) uniquely represents the entire dataset (e.g., a folder or directory).

How It Works

1. Hashing Files:
   • Each file in a folder is hashed (e.g., CRC, SHA-1).
   • Example: File A → Hash_A, File B → Hash_B.
2. Building the Tree:
   • Combine pairs of hashes and hash them again to create parent nodes.
   • Repeat until a single root hash is generated.

   Root Hash = Hash(Hash_A + Hash_B)
   ├── Hash_A (File A)
   └── Hash_B (File B)
   

   For larger datasets, the tree becomes deeper:

   Root Hash = Hash(Hash(Hash_A + Hash_B) + Hash(Hash_C + Hash_D))
   ├── Hash(Hash_A + Hash_B)
   │   ├── Hash_A (File A)
   │   └── Hash_B (File B)
   └── Hash(Hash_C + Hash_D)
       ├── Hash_C (File C)
       └── Hash_D (File D)
   

3. Comparing Trees:
   • To compare two folders, compare their root hashes.
   • If root hashes match, the entire folder structure is identical.
   • If they differ, traverse the tree to identify which subtree (or file) changed.

---

2. Key Features

• Efficient Comparison: Identifies differences in O(log n) time instead of checking every file (O(n)).
• Tamper Detection: Any change to a file propagates up the tree, altering the root hash.
• Partial Verification: You can verify specific subtrees without needing the entire dataset.

---

3. Scenarios & Applications

1. Version Control Systems (e.g., Git)

• Git uses a Merkle Tree-like structure to track file changes. Each commit generates a root hash, enabling efficient diffs between versions.

2. File Synchronization (e.g., Dropbox, rsync)

• Detect changes in large folders by comparing root hashes. Only sync modified subtrees.

3. Blockchain & Distributed Systems

• Bitcoin and Ethereum use Merkle Trees to verify transactions in blocks. Light nodes validate blocks without storing the entire blockchain.

4. Backup Systems

• Identify incremental changes between backups by comparing root hashes.

5. Peer-to-Peer Networks (e.g., BitTorrent)

• Verify file integrity during downloads by checking hashes of chunks against the root hash.

6. Anti-Virus Scanners

• Quickly verify system files against known-good Merkle Trees to detect tampering.

---

4. Example Workflow

Suppose you have two folders, Folder A and Folder B:

1. Build a Merkle Tree for each folder.
2. Compare root hashes:
   • Match: Folders are identical.
   • Mismatch: Traverse the tree to find conflicting subtrees/files.
     • Example: If Hash(Hash_A + Hash_B) in Folder A differs from Folder B, check Hash_A and Hash_B to pinpoint the changed file.

---

5. Limitations

• Collision Risk: Weak hashes (e.g., CRC) can produce the same hash for different files. Use cryptographic hashes (SHA-256) for security.
• Overhead: Building the tree adds computational cost upfront.

---

Alternatives

• Flat Hash List: A simple list of file hashes (less efficient for large datasets).
• Content-Defined Chunking: Used in tools like borg or restic for deduplication.

Merkle Trees are foundational in systems where data integrity, efficient comparison, and tamper-proofing are critical. Let me know if you’d like to dive deeper into a specific use case!