<scp>ReSBM:</scp> Region-based Scale and Minimal-Level Bootstrapping Management for FHE via Min-Cut

The RNS-CKKS scheme in Fully Homomorphic Encryption (FHE) supports crucial features for privacy-preserving machine learning, such as fixed-point arithmetic and SIMD-style vectorization. Yet, managing the escalation of ciphertext scales from homomorphic multiplications, which risks capacity overflow, along with bootstrapping, presents significant challenges. These complexities are exacerbated by the need to efficiently handle scale and bootstrapping at compile time while ensuring rapid encrypted inference.

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Publication Details

Published
2025-02-06
DOI
https://doi.org/10.1145/3669940.3707276
Citations
7
Primary Topic
Cryptography and Data Security
Type
article
Field-Weighted Citation Impact
17.37
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article

ReSBM: Region-based Scale and Minimal-Level Bootstrapping Management for FHE via Min-Cut

Linjie Xiao, X. D. Zhang, Jun Lai, Jingling Xue et al.
7 citations
Cryptography and Data Security
17.37
article

ReSBM: Region-based Scale and Minimal-Level Bootstrapping Management for FHE via Min-Cut

Linjie Xiao, X. D. Zhang, Jun Lai, Jingling Xue, Yan Liu, Wenguang Chen, Long Li, Qing Zhu, Peng Yuan, Tianxiang Sui
article en
7 citations

Abstract

The RNS-CKKS scheme in Fully Homomorphic Encryption (FHE) supports crucial features for privacy-preserving machine learning, such as fixed-point arithmetic and SIMD-style vectorization. Yet, managing the escalation of ciphertext scales from homomorphic multiplications, which risks capacity overflow, along with bootstrapping, presents significant challenges. These complexities are exacerbated by the need to efficiently handle scale and bootstrapping at compile time while ensuring rapid encrypted inference.

UNSW Sydney (AU), Tsinghua University (CN)
Openalex Percentile: Top 1%
Cryptography and Data Security
17.37
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