CRMay 8

Reliable Non-Leveled Homomorphic Encryption for Web Services

arXiv:2508.029434.31 citationsh-index: 1
Predicted impact top 89% in CR · last 90 daysOriginality Incremental advance
AI Analysis

For web services needing data confidentiality, this work addresses FHE's computational overhead and error correction bottlenecks, but the prototype is preliminary and not fully validated.

The paper proposes a new FHE framework with automatic error correction and improved efficiency, achieving failure rates below 0.5% under bursty faults and accuracy within 0.5 percentage points of plaintext baseline in simulations.

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 prototype is evaluated through encrypted low-degree activation timing, one experimental public Refresh skeleton invocation, and transport-fault simulations for the Ring--BCH layer. Our current prototype quantifies the cost of encrypted low-degree activation evaluation, the additional latency of an experimental public Refresh skeleton, and the robustness gained from the Ring--BCH transport layer. The Refresh prototype should be interpreted as a skeleton rather than a complete CKKS bootstrapping implementation, since it uses a low-degree surrogate rather than a validated EvalMod circuit. In transport-fault simulations, the BCH interleaver reduces failure rates to below $0.5\%$ under bursty faults and keeps the modeled accuracy within $0.5$ percentage points of the plaintext baseline.

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