Building Privacy-Preserving AI system: AI Inference on Encrypted Data with Fully Homomorphic Encryption
Riccardo Magni, Carlo Tassi, Emanuele D’Agostini · 2025
Artificial intelligence (AI) has revolutionized business strategies by enhancing service personalization and decision-making processes. However, deploying these technologies requires significant computational power, often sourced from third-party cloud services. These services facilitate complex calculations for training and inferring machine learning models without requiring substantial hardware investments. The risk of data exposure or unauthorized access remains high, particularly for sensitive and confidential information.Fully Homomorphic Encryption (FHE) [1] offers a promising solution by enabling mathematical operations on encrypted data without decryption, ensuring total data confidentiality and regulatory compliance.This paper aims to explore the application of an FHE-based Machine Learning framework to real-world scenarios, focusing on reliability, practical performance and feasibility of use in production environments, in comparison with building and making inferences on unencrypted models. The framework selected for this study is Zama Concrete ML [2], a machine learning framework based on Fully Homomorphic Encryption over the Torus (TFHE) [3].The results obtained demonstrate the progress achieved by FHE, indicating that this technology is now mature enough to be applied in real-world contexts. Although the high costs associated with this technology — including computation time, circuit construction, key management, and encrypted data transfer — can be significant, in some cases it enables computations that would otherwise be impossible due to the inability to share sensitive data with third-party service providers.