Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse Keys

Abstract

We present a bootstrapping procedure for the full-RNS variant of the approximate homomorphic-encryption scheme of Cheon et al., CKKS (Asiacrypt 17, SAC 18). Compared to the previously proposed procedures (Eurocrypt 18 & 19, CT-RSA 20), our bootstrapping procedure is more precise, more efficient (in terms of CPU cost and number of consumed levels), and is more reliable and 128-bit-secure. Unlike the previous approaches, it does not require the use of sparse secret-keys. Therefore, to the best of our knowledge, this is the first procedure that enables a highly efficient and precise bootstrapping with a low probability of failure for parameters that are 128-bit-secure under the most recent attacks on sparse R-LWE secrets. We achieve this efficiency and precision by introducing three novel contributions: (i) We propose a generic algorithm for homomorphic polynomial-evaluation that takes into account the approximate rescaling and is optimal in level consumption. (ii) We optimize the key-switch procedure and propose a new technique for linear transformations (double hoisting). (iii) We propose a systematic approach to parameterize the bootstrapping, including a precise way to assess its failure probability. We implemented our improvements and bootstrapping procedure in the open-source Lattigo library. For example, bootstrapping a plaintext in $\mathbb{C}^{32768}$ takes 18s, has an output coefficient modulus of 505 bits, a mean precision of 19.1 bits, and a failure probability of $2^{-15.58}$. Hence, we achieve 14.1× improvement in bootstrapped throughput (plaintext-bit per second), with respect to the previous best results, and we have a failure probability 468× smaller and ensure 128-bit security.

Publication
Advances in Cryptology – EUROCRYPT 2021
Jean-Philippe Bossuat
Jean-Philippe Bossuat
Cryptography Research Scientist & Software Specialist

My research interests include applied lattice based cryptography, privacy preserving machine learning and secure analytics.