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Cited by 284

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

EnclaveDB: A Secure Database Using SGX

Priebe, Christian, Vaswani, Kapil, Costa, Manuel·2018·2018 IEEE Symposium on Security and Privacy (SP)

Abstract

We propose EnclaveDB, a database engine that guarantees confidentiality, integrity, and freshness for data and queries. EnclaveDB guarantees these properties even when the database administrator is malicious, when an attacker has compromised the operating system or the hypervisor, and when the database runs in an untrusted host in the cloud. EnclaveDB achieves this by placing sensitive data (tables, indexes and other metadata) in enclaves protected by trusted hardware (such as Intel SGX). EnclaveDB has a small trusted computing base, which includes an in-memory storage and query engine, a transaction manager and pre-compiled stored procedures. A key component of EnclaveDB is an efficient protocol for checking integrity and freshness of the database log. The protocol supports concurrent, asynchronous appends and truncation, and requires minimal synchronization between threads. Our experiments using standard database benchmarks and a performance model that simulates large enclaves show that EnclaveDB achieves strong security with low overhead (up to 40% for TPC-C) compared to an industry strength in-memory database engine.

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Metadata

Authors
Priebe, C., Vaswani, K., & Costa, M.
Venue
2018 IEEE Symposium on Security and Privacy (SP)
Publisher
IEEE
Published
2018-05
Pages
264-278
ISSN
1081-6011 (ISSN-L)
OpenAlex
W2788385473
Cited by
284
References
39
FWCI
14.36
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: EnclaveDB: A Secure Database Using SGX. https://app.cochat.ai/papers/10.1109/sp.2018.00025
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Priebe, C., Vaswani, K., & Costa, M. (2018). EnclaveDB: A Secure Database Using SGX. 2018 IEEE Symposium on Security and Privacy (SP). https://doi.org/10.1109/sp.2018.00025

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.