Quantization of Neural Networks for Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) offers a promising solution to the challenge of maintaining trust and privacy in machine learning by enabling computations on encrypted data without compromising security, as only the legitimate data owner can decrypt the results. This technique faces challenges, such as the complexity of handling different neural network architectures and limitations in precision, which can be addressed through quantization. Quantization transforms neural networks from floating-point to integer values, thereby improving efficiency and aligning with the constraints of FHE. This process involves using techniques like Programmable Bootstrapping (PBS) and affine quantization to maintain accuracy while allowing operations on small integers, which are essential for FHE-friendly models. The article highlights ongoing developments in FHE, such as the compilation of neural networks using tools like PyTorch and Concrete Numpy, aiming to achieve efficient end-to-end encryption for AI applications.