Fast Hash Functions for Encrypted Data

TL;DR

For indexing and searching encrypted data quickly, use SHA-256 or BLAKE3. They offer a good balance of speed, security, and collision resistance. Avoid MD5 and SHA-1 as they are considered insecure for this purpose.

Choosing the Right Hash Function

When you need to search encrypted data, you can’t directly look at the content. Instead, you hash it (turn it into a unique fingerprint) and index that. Here’s how to pick the best hash function for this job:

1. Understand Your Requirements

1. Speed: Hashing needs to be fast because you’ll do it often, both when adding data and searching.
2. Security: The hash function must be resistant to collisions (different data producing the same hash) and pre-image attacks (finding data that produces a specific hash). This protects your index from being exploited.
3. Collision Resistance: While perfect collision resistance isn’t achievable, you want a low probability of different encrypted files hashing to the same value.

2. Hash Functions to Consider

1. SHA-256: A widely used and well-respected hash function. It’s generally fast enough for most applications and is considered secure.
2. BLAKE3: A newer hash function designed for speed, especially on modern CPUs. Often faster than SHA-256 without sacrificing security.
3. SHA-3 (Keccak): Another strong contender, but often slower than SHA-256 and BLAKE3.
4. MD5 & SHA-1: Do not use these! They have known vulnerabilities and are easily exploited. Collisions can be found relatively quickly, making your index insecure.

3. Implementation Examples

Here’s how you might hash data using Python:

Python – SHA-256

import hashlib

def sha256_hash(data):
  hasher = hashlib.sha256()
  hasher.update(data)
  return hasher.hexdigest()

# Example usage:
data = b'This is some sensitive data'
hash_value = sha256_hash(data)
print(f"SHA-256 Hash: {hash_value}")

Python – BLAKE3

import blake3

def blake3_hash(data):
  hasher = blake3.blake3()
  hasher.update(data)
  return hasher.hexdigest()

# Example usage:
data = b'This is some sensitive data'
hash_value = blake3_hash(data)
print(f"BLAKE3 Hash: {hash_value}")

4. Indexing and Searching

1. Hash the Data: When you store encrypted data, immediately hash it using your chosen function (SHA-256 or BLAKE3).
2. Store the Hash: Store this hash value in an index (e.g., a database table) alongside metadata about the file.
3. Search by Hash: When searching, hash the search query and look for matching hashes in your index.

5. Important Considerations

• Salt (Optional but Recommended): Add a unique random ‘salt’ to each piece of data before hashing. This makes it harder for attackers to use pre-computed hash tables (rainbow tables).
• Key Derivation Functions (KDFs): If you are hashing passwords or other secrets, use a KDF like PBKDF2, scrypt, or Argon2 instead of a simple hash function. These are specifically designed for security in that context.
• Regularly Review: cyber security best practices evolve. Stay updated on the latest recommendations for hash functions and cryptographic algorithms.