Secure and Policy-Compliant Query Processing on Heterogeneous Computational Storage Architectures
Harshavardhan Unnibhavi, David Cerdeira, Antonio Barbalace, Nuno Santos, Pramod Bhatotia
摘要
Computation Storage Architectures (CSA) are increasingly adopted in the cloud for near data processing, where the underlying storage devices/servers are now equipped with heterogeneous cores which enable computation offloading near to the data. While CSA is a promising high-performance architecture for the cloud, in general data analytics also presents significant data security and policy compliance (e.g., GDPR) challenges in untrusted cloud environments. In this paper, we present IronSafe, a secure and policy-compliant query processing system for heterogeneous computational storage architectures, while preserving the performance advantages of CSA in untrusted cloud environments. To achieve these design properties in a computing environment with heterogeneous host (x86) and storage system (ARM), we design and implement the entire hardware and software system stack from the ground-up leveraging hardware-assisted Trusted Execution Environments (TEEs): namely, Intel SGX and ARM TrustZone. More specifically, IronSafe builds on three core contributions: (1) a heterogeneous confidential computing framework for shielded execution with x86 and ARM TEEs and associated secure storage system for the untrusted storage medium; (2) a policy compliance monitor to provide a unified service for attestation and policy compliance; and (3) a declarative policy language and associated interpreter for concisely specifying and efficiently evaluating a rich set of polices. Our evaluation using the TPC-H SQL benchmark queries and GDPR anti-pattern use-cases shows that IronSafe is faster, on average by 2.3x than a host-only secure system, while providing strong security and policy-compliance properties.
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引用它的顶会 Paper3
- TEE-based General-purpose Computational Backend for Secure Delegated Data ProcessingMo Sha, Jialin Li, Sheng Wang, Feifei Li 等SIGMOD 2024 · 被引用 6 次
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- SeSeMI: Secure Serverless Model Inference on Sensitive DataGuoyu Hu, Yuncheng Wu, Gang Chen, Tien Tuan Anh Dinh 等ICDE 2025 · 被引用 1 次
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- Oblivious Multi-Party Machine Learning on Trusted ProcessorsOlga Ohrimenko, Felix Schuster, Cédric Fournet, Aastha Mehta 等USENIX Security 2016 · 被引用 594 次
- EnclaveDB: A Secure Database Using SGXChristian Priebe, Kapil Vaswani, Manuel CostaS&P 2018 · 被引用 329 次
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- OBLIVIATE: A Data Oblivious Filesystem for Intel SGXAdil Ahmad, Kyungtae Kim, Muhammad Ihsanulhaq Sarfaraz, Byoungyoung LeeNDSS 2018 · 被引用 144 次
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