4.4 Article

ObliDB: Oblivious Query Processing for Secure Databases

期刊

PROCEEDINGS OF THE VLDB ENDOWMENT
卷 13, 期 2, 页码 169-183

出版社

ASSOC COMPUTING MACHINERY
DOI: 10.14778/3364324.3364331

关键词

-

资金

  1. Facebook
  2. Google
  3. Infosys
  4. Intel
  5. Microsoft
  6. NEC
  7. SAP
  8. Teradata
  9. VMware
  10. NSF [CNS-1651570]
  11. DARPA/ARL SAFEWARE project
  12. Simons foundation
  13. ONR

向作者/读者索取更多资源

Hardware enclaves such as Intel SGX are a promising technology for improving the security of databases outsourced to the cloud. These enclaves provide an execution environment isolated from the hypervisor/OS, and encrypt data in RAM. However, for applications that use large amounts of memory, including most databases, enclaves do not protect against access pattern leaks, which let attackers gain a large amount of information about the data. Moreover, the naive way to address this issue, using Oblivious RAM (ORAM) primitives from the security literature, adds substantial overhead. A number of recent works explore trusted hardware enclaves as a path toward secure, access-pattern oblivious outsourcing of data storage and analysis. While these works efficiently solve specific subproblems (e.g. building secure indexes or running analytics queries that always scan entire tables), no prior work has supported oblivious query processing for general query workloads on a DBMS engine with multiple access methods. Moreover, applying these techniques individually does not guarantee that an end-to-end workload, such as a complex SQL query over multiple tables, will be oblivious. In this paper, we introduce ObliDB, an oblivious database engine design that is the first system to provide obliviousness for general database read workloads over multiple access methods. ObliDB introduces a diverse array of new oblivious physical operators to accelerate oblivious SQL queries, giving speedups of up to an order of magnitude over naive ORAM. It supports a broad range of queries, including aggregation, joins, insertions, deletions and point queries. We implement ObliDB and show that, on analytics workloads, ObliDB ranges from 1.1-19 x faster than Opaque, a previous oblivious, enclave-based system designed only for analytics, and comes within 2.6 x of Spark SQL, which provides no security guarantees. In addition, ObliDB supports point queries with 3-10ms latency, which is comparable to index-only trusted hardware systems, and runs over 7x faster than HIRB, a previous encryption-based oblivious index system that supports point queries.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据