TFHE Deep Dive - Part IV - Programmable Bootstrapping

Zama's blog post series explores the intricacies of the Fully Homomorphic Encryption (FHE) scheme known as TFHE, specifically focusing on its implementation in Rust through TFHE-rs. The series covers various aspects, including ciphertext types, encodings, and operations like key switching and modulus switching, culminating in an in-depth examination of programmable bootstrapping. Bootstrapping, a pivotal operation in FHE, is dissected into steps involving modulus switching, blind rotation, and sample extraction to reduce noise and enable large-scale homomorphic computations. The post highlights applications of TFHE in neural networks, demonstrating its potential for secure, homomorphic evaluation of deep learning models. Readers are encouraged to engage with the open-source TFHE-rs project, and the post concludes by expressing gratitude to contributors and inviting the community to support and participate in ongoing FHE challenges.