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PRICE: Privacy-Preserving Image Classification in Mobile Edge-Cloud Computing with Homomorphic Encryption

Duc-Huy Nguyen, Bui Duc Manh, Chi-Hieu Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Van-Dinh Nguyen, Quoc‐Viet Pham · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.25286 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

基于云计算的基层图像分类可以实现许多移动AI应用,但在云计算中传输原始图像会引起重大隐私问题。Homomorphic pay-encrypting(HE)允许计算在低加密数据上;然而,现有的端到端的HE基层推断管道会产生禁用延迟和计算上限,限制其对延迟敏感移动系统的可行性。本文讨论了如何在移动边界云环境中实现高效的低加密图像分类,而不牺牲精度。我们提出了PRICE,一个三层边界云架构,在可信边界和不受信任的移动云之间分离深度神经网络推断。边界在Halext进行轻量级的转换图像分类,而只有嵌入式的云计算机

Machine translation (Meta M2M100); the English original follows.

Abstract

Cloud-assisted image classification enables many mobile AI applications, but transmitting raw images to the cloud raises significant privacy concerns. Homomorphic encryption (HE) allows computation over encrypted data; however, existing end-to-end HE-based inference pipelines incur prohibitive latency and computational overhead, limiting their feasibility for latency-sensitive mobile systems. This paper addresses how to enable efficient encrypted image classification in mobile edge-cloud environments without sacrificing accuracy. We propose PRICE, a three-tier edge-cloud architecture that partitions deep neural network inference between a trusted edge and an untrusted cloud. The edge performs lightweight feature extraction in plaintext, while only compact embeddings are encrypted using the Cheon-Kim-Kim-Song (CKKS) scheme and offloaded to the cloud for encrypted final-layer inference. This design avoids homomorphic evaluation of deep backbone layers, significantly reducing computation depth, ciphertext size, and end-to-end latency. To further improve efficiency, we develop RowHyDiag, a row-packed hybrid diagonal encoding with hoisted-rotation-aware encrypted matrix-vector computation, which minimizes rotation operations and improves slot utilization. Extensive experiments on real devices across five datasets demonstrate that, under the evaluated 128-bit-security configurations, RowHyDiag achieves up to 31.8$\times$ faster cloud inference, 85.3$\times$ fewer rotations, and 83.0$\times$ lower setup payload than the conventional Halevi-Shoup diagonal method. RowHyDiag also outperforms the PEGASUS linear-transformation baseline, providing 1.1-4.5$\times$ faster cloud inference and 1.2-3.4$\times$ lower setup payload. The results show that PRICE enables deployment-ready, low-latency encrypted inference for mobile edge-cloud systems under a trusted-edge threat model.

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Topics: Computer science, Homomorphic encryption, Encryption, Cloud computing, Ciphertext, Payload (computing)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange