Paper 2022/162

On the precision loss in approximate homomorphic encryption

Abstract

Since its introduction at Asiacrypt 2017, the CKKS approximate homomorphic encryption scheme has become one of the most widely used and implemented homomorphic encryption schemes. Due to the approximate nature of the scheme, application developers using CKKS must ensure that the evaluation output is within a tolerable error of the corresponding plaintext computation. Choosing appropriate parameters requires a good understanding of how the noise will grow through the computation. A strong understanding of the noise growth is also necessary to limit the performance impact of mitigations to the attacks on CKKS presented by Li and Micciancio (Eurocrypt 2021). In this work we present a comprehensive noise analysis of CKKS, that considers noise coming both from the encoding and homomorphic operations. Our main contribution is the first average-case analysis for CKKS noise, and we also introduce refinements to prior worst-case noise analyses. We develop noise heuristics both for the original CKKS scheme and the RNS variant presented at SAC 2018. We then evaluate these heuristics by comparing the predicted noise growth with experiments in the HEAAN and FullRNS-HEAAN libraries, and by comparing with a worst-case noise analysis as done in prior work. Our findings show mixed results: while our new analyses lead to heuristic estimates that more closely model the observed noise growth than prior approaches, the new heuristics sometimes slightly underestimate the observed noise growth. This evidences the need for implementation-specific noise analyses for CKKS, which recent work has shown to be effective for implementations of similar schemes.