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: $ 50万
• 依托单位: University of California-Irvine
• 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
• 起止时间: 2011-08-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1118127&HistoricalAwards=false
• 关键词: TC; Small; Risk; Aware; Query

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.

在数据库查询处理期间，数据在不同的系统组件之间迁移，每个系统组件可能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，数据迁移，特别是从安全组件到相对不安全的组件的迁移，可能会增加数据丢失的风险。这项建议对安全采取了“基于风险”的方法，其中，拟议的研究不是设计防止攻击的方法，而是在查询处理期间通过各种组件控制数据流，以在暴露风险和系统性能之间取得平衡。有风险意识的查询处理技术在两个环境中被探索：(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存储器中。(B)云计算环境，在高峰负载期间，查询(和相应的数据)从(相对安全的)私有存储被运送到(相对不安全的)公共云基础设施进行处理，这种现象称为云突发。在这两种设置中，探讨了共同优化查询执行以同时最小化泄漏风险和性能成本的技术。该研究提供了一种基于预防攻击的传统技术的补充方法，以支持数据库系统环境中的实际安全。该项目寻求帮助启动数据库安全研究的新方向，探索限制风险的技术(而不仅仅是预防攻击)。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，从而提高其采用率，即使是对于存在重大数据机密性问题的应用程序也是如此。最后，计划中的研究、系统开发和教育活动有望显著提升学生的教育体验。