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TC: Small: Risk Aware Query Processing in Mixed Security Database Environments

TC: Small: Risk Aware Query Processing in Mixed Security Database Environments
TC:小型:混合安全数据库环境中的风险感知查询处理
批准号:
1118127
负责人:
Sharad Mehrotra
金额:
$50.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-01 至 2015-09-30
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项目摘要

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中文摘要
翻译
在数据库查询处理期间,数据跨不同的系统组件迁移,每个系统组件可以提供不同级别的安全保证,并且可能容易受到不同的攻击。如果底层数据(或部分数据)是敏感的,则数据迁移(尤其是从安全组件迁移到相对不安全的组件)可能会增加数据丢失的风险。该提案采取了“基于风险”的安全方法,其中,而不是设计方法来防止攻击,所提出的研究控制数据流在查询处理过程中通过各种组件,以这种方式达到风险暴露和系统性能之间的平衡。风险感知查询处理技术在两种情况下进行了探索:(a)独立的数据库服务器,其中磁盘上的数据被加密存储(因此是安全的),但在查询处理期间以明文加载到内存中。(b)在云计算环境中,在峰值负载期间,查询(和相应的数据)从(相对安全的)私有存储中运送到(相对不安全的)公共云基础设施中进行处理,这种现象称为云爆发。在这两种情况下,共同优化查询执行,同时最大限度地减少披露风险以及性能成本的技术进行了探讨。该研究为基于防止攻击的传统技术提供了一种补充方法,以支持数据库系统环境中的实际安全性。该项目旨在帮助启动数据库安全研究的新方向,探索限制风险的技术(而不仅仅是防止攻击)。这项研究如果成功,将使基于云的数据管理解决方案更加安全,即使对于有大量数据机密性问题的应用程序,也会提高其采用率。最后,计划的研究,系统开发和教育活动预计将显着提高学生的教育经验。
英文摘要
During database query processing, data migrates across different system components each of which may offer different levels of security guarantees and may be susceptible to different attacks. If the underlying data (or part of it) is sensitive, data migration, especially from secure components to those that are relatively insecure could increase risk of data loss. This proposal takes a "risk-based" approach to security wherein instead of designing approaches to prevent attacks, the proposed research controls flow of data during query processing through various components in such a way to strike a balance between risk of exposure and system performance. Risk-aware query processing techniques is explored in two settings: (a) A stand-alone database server where data on disks is stored encrypted (and is hence secure) but is loaded in plaintext into memory during query processing. (b) Cloud computing environment where, during peak load queries (and corresponding data) are shipped from (relatively secure) private storage to be processed at (relatively insecure) public cloud infrastructure, a phenomena known as cloudbursting. In both these settings, techniques to co-optimize query execution to simultaneously minimize both disclosure risks as well as performance costs are explored. The research offers a complementary approach to traditional techniques based on preventing attacks to support practical security in the context of database systems.The project seeks to help launch a new direction to database security research that explores techniques to limit risks (instead of only preventing attacks). The research, if successful, will make cloud-based data management solutions more secure increasing the rate of their adoption even for applications that have substantial data confidentiality concerns. Finally, the planned research, system development, and educational activities are expected to significantly enhance the educational experience of students.
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