cyber-security-resources/ai_research/AI Security Best Practices/homomorphic-encryption.md

Homomorphic Encryption

Homomorphic encryption is a form of encryption allowing one to perform calculations on encrypted data without decrypting it first. The result of the computation is in encrypted form, and when decrypted, it matches the result of the operation as if it had been performed on the plain text.

This method is beneficial for privacy-preserving computations on sensitive data. It is especially useful for cloud computing, where you can process your data on third-party servers without revealing any sensitive information to those servers.

Although promising, homomorphic encryption is computationally intensive and not yet practical for all applications. Researchers are working on improving the efficiency of these methods, and we can expect their usage to increase in the future.

The following is a simple example of addition and multiplication operations using homomorphic encryption with Python and a library called Pyfhel, which stands for Python for Fully Homomorphic Encryption Libraries. In this example, we will encrypt two integers, perform addition and multiplication operations on the encrypted data, and then decrypt the results.

Install the Pyfhel library:

pip install Pyfhel

Here is the simple Python code:

from Pyfhel import Pyfhel, PyCtxt

# Create a Pyfhel object
HE = Pyfhel()

# Generate a public and secret key
HE.keyGen()

# Encrypt two numbers
num1 = 5
num2 = 10
enc_num1 = HE.encryptInt(num1)
enc_num2 = HE.encryptInt(num2)

# Perform addition operation on encrypted numbers
enc_result_add = enc_num1 + enc_num2

# Perform multiplication operation on encrypted numbers
enc_result_mul = enc_num1 * enc_num2

# Decrypt the results
result_add = HE.decryptInt(enc_result_add)
result_mul = HE.decryptInt(enc_result_mul)

print(f"Decrypted addition result: {result_add}, Expected: {num1+num2}")
print(f"Decrypted multiplication result: {result_mul}, Expected: {num1*num2}")

This script creates an instance of Pyfhel, generates a public and secret key with keyGen(), encrypts two integers using encryptInt(), adds and multiplies them, then decrypts the results using decryptInt(). The decrypted results should be equal to the results of adding and multiplying the original, unencrypted numbers.

Remember that this is a simplified example. In a real-world scenario, key management and ensuring the security of the encryption and decryption operations are crucial and more complex. Furthermore, full homomorphic encryption is a computationally intensive task and may not be suitable for all types of data or applications.

References

A few resources that can provide a deeper understanding of homomorphic encryption:

1. Homomorphic Encryption Standard: The official site for the Homomorphic Encryption Standard, containing detailed technical resources and documentation.

2. Homomorphic Encryption Notations, Schemes, and Circuits: A technical paper providing a more mathematical and in-depth exploration of various homomorphic encryption schemes.

3. Cryptonets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy: A research paper from Microsoft Research demonstrating the application of homomorphic encryption in machine learning.

4. Pyfhel Github Repository: The Github repository for Pyfhel, a Python library for Homomorphic Encryption, which includes code examples and documentation.

Homomorphic encryption is a complex field that requires a decent understanding of cryptography. It's recommended to have a good grasp of the basics of cryptography before diving into homomorphic encryption.