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The recent development of Trusted Execution Environment has brought unprecedented opportunities for confidential computing within cloud-based systems. Among various popular cloud business models, serverless computing has gained dominance since its emergence, leading to a high demand for confidential serverless computing services based on trusted enclaves. However, the issue of cold start overhead significantly hinders its performance, as new enclaves need to be created to ensure a clean and verifiable execution environment. In this paper, we propose a novel approach for constructing reusable enclaves that enable rapid enclave reset and robust security with three key enabling techniques: enclave snapshot and rewinding, nested attestation, and multi-layer intra-enclave compartmentalisation. We have built a prototype system for confidential serverless computing, integrating OpenWhisk and a WebAssembly runtime, which significantly reduces the cold start overhead in an end-to-end serverless setting while imposing a reasonable performance impact on standard execution.
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Civet: An Efficient Java Partitioning Framework for Hardware Enclaves
Hardware enclaves are designed to execute small pieces of sensitive code or to operate on sensitive data, in isolation from larger, less trusted systems. Partitioning a large, legacy application requires significant effort. Partitioning an application written in a managed language, such as Java, is more challenging because of mutable language characteristics, extensive code reachability in class libraries, and the inevitability of using a heavyweight runtime. Civet is a framework for partitioning Java applications into enclaves. Civet reduces the number of lines of code in the enclave and uses language-level defenses, including deep type checks and dynamic taint-tracking, to harden the enclave interface. Civet also contributes a partitioned Java runtime design, including a garbage collection design optimized for the peculiarities of enclaves. Civet is efficient for data-intensive workloads; partitioning a Hadoop mapper reduces the enclave overhead from 10 to 16–22% without taint-tracking or 70–80% with taint-tracking.
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- PAR ID:
- 10180807
- Date Published:
- Journal Name:
- Proceedings of the 29th USENIX Security Symposium (USENIX Security '20)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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