SDXE: Accelerating Secure Cloud Deduplication Via SGX in Edge Computing
Jian Wu, Yinjin Fu, Nong Xiao · 2025
Secure data deduplication in edge computing has emerged as a pivotal technique to enhance the cost-efficiency and data security of cloud storage infrastructures. However, the prevailing methodologies predominantly rely on resource-intensive cryptographic operations, resulting in substantial communication and computation overheads. In this paper, we introduce SDXE: a secure data deduplication framework that leverages cloud-edge collaborative computing through Intel's Software Guard Extensions (SGX). It can strike a balance between system performance and data security by harnessing SGX to protect sensitive operations, circumventing the need for conventional cryptographic algorithms. Additionally, we propose a hierarchical storage strategy predicated on data temperature, alongside a version-based two-level fingerprint index structure, to optimize data storage efficiency and enhance transfer performance. Comparing with the typical cryptography-based cloud-edge collaborative secure deduplication schemes, our experimental results demonstrate that SDXE can significantly enhance data communication efficiency with high data security, achieving a remarkable$9.25 \times$upload throughput in client-edge stage,$3.44 \times$upload throughput in edge-cloud stage and$2.96 \times$download throughput.