January 2022 Summaries

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.

Zama has released Concrete Numpy as a public beta, which builds on their previous Homomorphic Numpy (HNP) prototype, offering an open-source compiler and a Numpy frontend designed to simplify the creation of encrypted programs for data scientists without requiring cryptographic expertise. Concrete Numpy improves upon HNP by inferring more information from functions, allowing users to define only the encrypted and clear inputs, while the program automatically determines shapes and bit sizes. The new version divides responsibilities, with data scientists handling model adaptation for Fully Homomorphic Encryption (FHE) constraints like low precision values, while Zama focuses on cryptographic tasks, enhancing accuracy and security. By adopting an exact approach, Concrete Numpy addresses previous precision challenges, supporting only integers and ensuring bit-exact computations. Zama continues to develop ML tools to ease data scientists' tasks and plans to release further resources and examples to aid in model optimization under FHE constraints.