Reliable Non-Leveled Homomorphic Encryption for Web Services

TL;DR

This paper introduces a new Fully Homomorphic Encryption framework that improves efficiency and adds automatic error correction, making FHE more practical for real-world web services.

Contribution

It proposes a novel FHE framework with encoding techniques and an algebraic reliability layer to enhance performance and robustness.

Findings

Evaluated encrypted low-degree activation timing.

Measured latency of a public Refresh skeleton.

Demonstrated robustness with BCH transport layer reducing failure rates.

Abstract

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.