一个名为ComputeFHE的新开源C++库已被开发出来，旨在简化使用全同态加密（FHE）的隐私保护应用程序的创建。该库构建在OpenFHE和TFHE密码系统之上，为开发人员提供了熟悉的加密数据操作编程范式。它旨在克服FHE相关的高计算成本和复杂性，通过优化的架构和减少的引导（bootstrapping）需求，在某些操作中展示了高达3.9倍的性能提升。 AI
影响 该库可能促使隐私保护技术在AI和其他数据敏感应用中得到更广泛的应用。
排序理由 该集群描述了一个基于研究论文的、用于隐私保护计算的新开源库。
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arXiv:2606.24379v1 Announce Type: cross Abstract: Fully Homomorphic Encryption (FHE) enables computations to be performed directly on encrypted data while preserving data confidentiality. However, its practical applications remain limited by high computational costs and developme…
Fully Homomorphic Encryption (FHE) enables computations to be performed directly on encrypted data while preserving data confidentiality. However, its practical applications remain limited by high computational costs and development complexity. This paper presents ComputeFHE, an …
Fully Homomorphic Encryption (FHE) enables computations to be performed directly on encrypted data while preserving data confidentiality. However, its practical applications remain limited by high computational costs and development complexity. This paper presents ComputeFHE, an …