USENIX Security2026Top-tier venue
Libra: Pattern-Scheduling Co-Optimization for Cross-Scheme FHE Code Generation over GPGPU
Song Bian, Yintai Sun, Zian Zhao, Haowen Pan, Mingzhe Zhang, Zhenyu Guan
Abstract
We propose Libra, a compiler framework that automates efficient code generation for cross-scheme fully homomorphic encryption (FHE) on highly parallel computing architectures. While it is known that leveraging multiple FHE schemes in a single application can improve the overall efficiency, the exact mapping of cross-scheme FHE operators onto high-performance architectures, such as general-purpose graphic processing units (GPGPUs), remains challenging. To address such challenge, Libra integrates both the FHE computational patterns and hardware-aware scheduling strategies to establish an algorithm-hardware co-optimization framework. Specifically, Libra defines a novel cross-scheme representation for FHE that abstracts common program patterns for each of the FHE schemes. Then, we dynamically optimize the output FHE program based on the combined execution costs of FHE primitives derived from multiple scheme switching patterns. Next, to accelerate inter-operator execution on GPUs, Libra introduces a computational scheduling strategy that bridges high-level computation characteristics with low-level execution plans. Through the proposed pattern-scheduling co-optimization process, Libra generates efficient codes for cross-scheme high-precision FHE computations on GPGPUs. Experiment results show that Libra achieves up to 270× speedup on microbenchmarks and 19× on the applications compared to state-of-the-art cross-scheme, while improving compute unit and memory bandwidth utilization by 44% and 36.1%.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext ed8b8418-c430-4e43-8a4a-3ab009187c28Builds on27
- Secure Outsourced Matrix Computation and Application to Neural NetworksXiaoqian Jiang, Miran Kim, Kristin E. Lauter, Yongsoo SongCCS 2018 · 359 citations
- Efficient Bootstrapping for Approximate Homomorphic Encryption with Non-sparse KeysJean-Philippe Bossuat, Christian Mouchet, Juan Ramón Troncoso-Pastoriza, Jean-Pierre HubauxEUROCRYPT 2021 · 179 citations
- On the Security of Homomorphic Encryption on Approximate NumbersBaiyu Li, Daniele MicciancioEUROCRYPT 2021 · 165 citations
- PEGASUS: Bridging Polynomial and Non-polynomial Evaluations in Homomorphic EncryptionWen-jie Lu, Zhicong Huang, Cheng Hong, Yiping Ma et al.S&P 2021 · 139 citations
- High-Precision Bootstrapping of RNS-CKKS Homomorphic Encryption Using Optimal Minimax Polynomial Approximation and Inverse Sine FunctionJoon-Woo Lee, Eunsang Lee, Yongwoo Lee, Young-Sik Kim et al.EUROCRYPT 2021 · 110 citations
Related papers
- HEIR: A Unified Representation for Cross-Scheme Compilation of Fully Homomorphic ComputationSong Bian, Zian Zhao, Zhou Zhang, Ran Mao et al.NDSS 2024
- CROPHE: Cross-Operator Dataflow Optimization for Fully Homomorphic Encryption AcceleratorsXinhua Chen, Jiangbin Dong, Hongren Zheng, Tian Tang et al.HPCA 2026 · 1 citation
- Cheddar: A Swift Fully Homomorphic Encryption Library Designed for GPU ArchitecturesWonseok Choi, Jongmin Kim, Jung Ho AhnASPLOS 2026 · 6 citations
- EVA: an encrypted vector arithmetic language and compiler for efficient homomorphic computationRoshan Dathathri, Blagovesta Kostova, Olli Saarikivi, Wei Dai et al.PLDI 2020 · 117 citations
- CHLOE: Loop Transformation over Fully Homomorphic Encryption via Multi-Level Vectorization and Control-Path ReductionSong Bian, Zian Zhao, Ruiyu Shen, Zhou Zhang et al.S&P 2025
