October 2021 Summaries

Optalysys explores the implementation of Conway's Game of Life using fully homomorphic encryption (FHE) with the Concrete library, developed by Zama, to demonstrate the potential of secure computation on encrypted data. FHE allows computations to be performed on encrypted data without needing decryption, offering significant security advantages, yet it is often complex to implement. Optalysys, although not specialists in cryptography, leveraged Concrete's user-friendly interface to execute complex FHE applications, such as the Game of Life, on their novel optical computing hardware. The Game of Life, a cellular automaton played on a grid with simple rules leading to complex behaviors, was successfully simulated entirely in encrypted space using Boolean operations. The article details the process of constructing Boolean algorithms for the simulation, including the development of an accumulator circuit to calculate cell states and the use of encrypted Boolean operations for updates, showcasing how Concrete-Boolean enables more accessible and efficient FHE applications.

Zama, a company focused on tackling privacy and security challenges, emphasizes building a team culture that values diversity, an additive culture, an excellence mindset, continuous learning, and pragmatism. Their hiring process, informed by the founders' past experiences, is tailored to identify candidates who align with these values. It begins with an application form designed to gauge creativity and motivation, followed by a challenge that assesses a candidate's passion and problem-solving abilities. Successful applicants proceed to a technical interview that tests their technical depth and cultural fit, culminating in a two-day trial at the Paris office to evaluate productivity and team compatibility. The final step involves reference checks to uncover any hidden qualities or red flags, ensuring that new hires are well-suited to Zama's ambitious goals. This thorough process aims to make new team members feel integrated from day one, ultimately fostering a collaborative environment to advance their mission of solving privacy issues.

Fully Homomorphic Encryption (FHE) is a cryptographic technique that enables computations to be performed on encrypted data without decryption, and the Homomorphic NumPy (HNP) library facilitates this by converting functions operating on NumPy arrays into their homomorphic equivalents. The blog post introduces an experimental compiler designed to transform classical NumPy programs into FHE circuits, highlighting the process using logistic regression and Recurrent Neural Networks (RNNs) as examples. While the HNP tool is experimental and not slated for further development, its purpose is to demonstrate FHE's progress and allow the community to experiment as developers work on a more stable compiler with enhanced performance. The article outlines the steps for using HNP, such as installing the library via a Docker image, defining NumPy functions, generating encryption keys, and performing encryption, evaluation, and decryption. Additionally, it explains how the Concrete library's programmable bootstrapping operator overcomes challenges in evaluating non-linear activation functions and noise accumulation in ciphertexts. Despite current limitations in precision and speed, the post is optimistic about FHE's future, predicting that by 2025, it will be widely usable across various internet applications.