With the ubiquitous deployment of web services, ensuring data confidentiality has become a challenging imperative. Fully Homomorphic Encryption (FHE) presents a powerful solution for processing encrypted data; however, its widespread adoption is severely constrained by two fundamental bottlenecks: substantial computational overhead and the absence of a built-in automatic error correction mechanism. These limitations render the deployment of FHE in real-world, complex network environments impractical. To address this dual challenge, this work puts forward a new FHE framework that enhances computational efficiency and integrates an automatic error correction capability through new encoding techniques and an algebraic reliability layer. Our system was validated across several web workloads, including encrypted inference on MNIST and CIFAR-10, federated aggregation with non-IID data, and streaming analytics on household power consumption data. Experimental results demonstrate significant performance improvements, particularly with large polynomial modulus degrees such as $N=8192$, while maintaining task accuracy within $0.5\%$ of the plaintext baseline. Furthermore, our error correction mechanism reduces the service failure rate to below $0.5\%$ even under harsh, bursty network fault conditions.

翻译：随着Web服务的广泛部署，确保数据机密性已成为一项具有挑战性的迫切需求。全同态加密为处理加密数据提供了强大的解决方案；然而，其广泛应用受到两个基本瓶颈的严重制约：巨大的计算开销以及缺乏内置的自动纠错机制。这些限制使得FHE在现实世界的复杂网络环境中部署变得不切实际。为应对这一双重挑战，本研究提出了一种新的FHE框架，该框架通过新型编码技术和代数可靠性层，既提升了计算效率，又集成了自动纠错能力。我们的系统在多个Web工作负载上进行了验证，包括MNIST和CIFAR-10上的加密推理、非独立同分布数据下的联邦聚合，以及家庭用电数据上的流式分析。实验结果表明，该系统在保持任务准确率与明文基线相差0.5%以内的同时，尤其在如$N=8192$这样的大多项式模数度下，实现了显著的性能提升。此外，即使在严酷的突发性网络故障条件下，我们的纠错机制也能将服务故障率降低至0.5%以下。