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Exploring Dynamic Task Loading in SGX-Based Distributed Computing
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DOI：10.1109/TSC.2021.3123511.png)
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Abstract
En 中文
Nowadays, data privacy is one of the most critical concerns in cloud computing, and many privacy-preserving distributed computing systems based on the trusted execution environment (e.g., Intel SGX) have been proposed to protect the user's privacy during cloud-outsourced computation. However, these SGX-based solutions are vulnerable to some traffic analyses, and loading all tasks into the enclave introduces much overhead for frequent EPC-paging. In this article, we propose a T-SGX framework, which keeps the confidentiality of a distributed job and guarantees the system efficiency by allowing dynamically loading an enclave shared object for the task under processing. In T-SGX, all these objects are secretly shared and stored in a verifiably distributed share management system (SMS) outside the TCB. To mitigate the exposure of sensitive information, we present an efficient oblivious transfer (OT) protocol under the Decisional Diffie-Hellman (DDH) assumption for obliviously transmitting desired shares. Detailed security analysis demonstrates that the proposed T-SGX achieves the goal of secure distributed computing without privacy leakage to unauthorized parties. Finally, we benchmark the framework in six real-world applications, and the experimental results show that T-SGX significantly outperforms a state-of-the-art solution, with 11.9%-29.7% less overhead performing an SGX-based application.
Keywords:
Cloud computing
Protocols
Codes
Task analysis
Servers
Security
Privacy
distributed computing framework
secret sharing
Intel SGX
privacy-preserving
Journal
IF:
5.8
Papers:
2.1K
Citations:
6.5K
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