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arxiv: 2508.11095 · v1 · pith:2WEENQOH · submitted 2025-08-14 · cs.CR

HEIR: A Universal Compiler for Homomorphic Encryption

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classification cs.CR
keywords heirencryptionhomomorphiccompilerresearchbuildingcompilerseffectively
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This work presents Homomorphic Encryption Intermediate Representation (HEIR), a unified approach to building homomorphic encryption (HE) compilers. HEIR aims to support all mainstream techniques in homomorphic encryption, integrate with all major software libraries and hardware accelerators, and advance the field by providing a platform for research and benchmarking. Built on the MLIR compiler framework, HEIR introduces HE-specific abstraction layers at which existing optimizations and new research ideas may be easily implemented. Although many HE optimization techniques have been proposed, it remains difficult to combine or compare them effectively. HEIR provides a means to effectively explore the space of HE optimizations. HEIR addresses the entire HE stack and includes support for various frontends, including Python. The contribution of this work includes: (1) We introduce HEIR as a framework for building HE compilers. (2) We validate HEIR's design by porting a large fraction of the HE literature to HEIR, and we argue that HEIR can tackle more complicated and diverse programs than prior literature. (3) We provide evidence that HEIR is emerging as the de facto HE compiler for academic research and industry development.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Detecting and Understanding Vulnerabilities in Fully Homomorphic Encryption Frameworks

    cs.CR 2026-06 unverdicted novelty 7.0

    HERTA is the first metamorphic-testing tool for FHE frameworks that found 21 previously unknown bugs across three industry frameworks, some already fixed by developers.

  2. Bifrost: Hybrid TEE-FHE Inference for Privacy-Preserving Transformer and LLM Serving

    cs.CR 2026-06 unverdicted novelty 7.0

    Bifrost achieves significant latency reductions in privacy-preserving transformer inference through a hybrid CPU TEE and accelerator FHE design, with Bifrost+ further optimizing via prefill/decode split.

  3. Adapting AlphaEvolve to Optimize Fully Homomorphic Encryption on TPUs

    cs.CR 2026-05 unverdicted novelty 7.0

    AlphaEvolve automates optimization of TFHE and CKKS FHE kernels on TPUv5e, finding changes that cut bootstrap latency by 2.5x and rotation/multiplication by 1.31x/1.18x versus human baselines.