RevokAll: Hardware-Assisted Revocable Data Sharing Framework for Full Data Traffic With Rapid Deployment in Cloud-Edge

Secure cloud-edge data sharing has been researched recently to provide high quality on-demand data service. Attribute-based encryption (ABE) is a promising solution that achieves data confidentiality and flexible access control simultaneously. But three major issues remain when adapting ABE in cloud-edge, namely reliable user revocation, high performance on devices, and trust issues of public cloud. First, existing direct user revocation mechanisms focus on preventing a revoked user from decrypting header ciphertexts even when key exposure occurs, but ignore the payload security. Second, how to conveniently apply deployment on diverse platforms and run programs on resource-constrained devices with high efficiency is a challenge. Finally, no universal guarantee of cloud computation and management tasks, thus lazy or malicious cloud may not follow the protocol and perform improper actions on purpose. In this work, we propose a Hardware-Assisted Hybrid Fully Outsourced Revocable Attribute-Based Proxy Re-Encryption (H²O-RABPRE) scheme that supports reliable user revocation for full data traffic and hardware-assisted fully outsourced computation. Moreover, we design a hardware-assisted data-sharing framework with rapid deployment for cloud-edge, which integrates the developed SGX-MCL to protect outsourced tasks executed by cloud/edge devices against malicious behaviors and utilizes the enhanced WebAssembly runtime, WasmCrypto, a unified deployment approach for IoT devices with near-native performance. We implement the scheme on an SGX cloud server, a laptop, a Raspberry Pi, and an ESP32 board, and the results indicate that the proposed scheme is practical.

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Publication Details

Journal
IEEE Transactions on Information Forensics and Security
Published
2025-01-01
DOI
https://doi.org/10.1109/tifs.2025.3583490
Citations
1
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
3.25

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article

RevokAll: Hardware-Assisted Revocable Data Sharing Framework for Full Data Traffic With Rapid Deployment in Cloud-Edge

Yuzhe Li, Hui Ma, Jianting Ning, Shuaishuai Chang et al.
1 citations
IEEE Transactions on Information Forensics and Security
Cloud Computing and Resource Management
3.25
article

RevokAll: Hardware-Assisted Revocable Data Sharing Framework for Full Data Traffic With Rapid Deployment in Cloud-Edge

Yuzhe Li, Hui Ma, Jianting Ning, Shuaishuai Chang, Bo Li, Weiping Wang, Lin Su
article en
1 citations

Abstract

Secure cloud-edge data sharing has been researched recently to provide high quality on-demand data service. Attribute-based encryption (ABE) is a promising solution that achieves data confidentiality and flexible access control simultaneously. But three major issues remain when adapting ABE in cloud-edge, namely reliable user revocation, high performance on devices, and trust issues of public cloud. First, existing direct user revocation mechanisms focus on preventing a revoked user from decrypting header ciphertexts even when key exposure occurs, but ignore the payload security. Second, how to conveniently apply deployment on diverse platforms and run programs on resource-constrained devices with high efficiency is a challenge. Finally, no universal guarantee of cloud computation and management tasks, thus lazy or malicious cloud may not follow the protocol and perform improper actions on purpose. In this work, we propose a Hardware-Assisted Hybrid Fully Outsourced Revocable Attribute-Based Proxy Re-Encryption (H²O-RABPRE) scheme that supports reliable user revocation for full data traffic and hardware-assisted fully outsourced computation. Moreover, we design a hardware-assisted data-sharing framework with rapid deployment for cloud-edge, which integrates the developed SGX-MCL to protect outsourced tasks executed by cloud/edge devices against malicious behaviors and utilizes the enhanced WebAssembly runtime, WasmCrypto, a unified deployment approach for IoT devices with near-native performance. We implement the scheme on an SGX cloud server, a laptop, a Raspberry Pi, and an ESP32 board, and the results indicate that the proposed scheme is practical.

IEEE Transactions on Information Forensics and SecurityVol. 20
Chinese Academy of Sciences (CN), Wuhan University (CN), Institute of Information Engineering (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 8%
Cloud Computing and Resource Management
3.25
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