Secure Machine Learning Inference with Homomorphic Encryption and ECC

Introduction

In the age of big data and privacy concerns, the need for secure computation methods is more pressing than ever. Homomorphic encryption (HE) allows computations to be performed on encrypted data without the need to decrypt it, preserving data confidentiality. When combined with cryptographic techniques like Elliptic Curve Cryptography (ECC), we can also ensure data integrity and authenticity.

This comprehensive guide explores the mathematical foundations of homomorphic encryption and ECC, dives into the implementation details with Python code, and demonstrates how to perform secure machine learning inference while ensuring both data privacy and model integrity.

Homomorphic Encryption

Definition and Properties

Homomorphic Encryption (HE) is a form of encryption that permits computation on ciphertexts, generating an encrypted result that, when decrypted, matches the result of operations performed on the plaintexts.

Properties:

• Additive Homomorphism