TWC: Small: Secure Data-Intensive Computing on Hybrid Clouds

TWC：小型：混合云上的安全数据密集型计算

• 批准号: 1223495
• 负责人: XiaoFeng Wang
• 金额: $ 50万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1223495&HistoricalAwards=false
• 关键词: TWC; Small; Secure; Data; Intensive

The ongoing effort to move data intensive computation to low-cost public clouds has been impeded by privacy concerns, as today's cloud providers offer little assurance for the protection of sensitive user data. This problem cannot be addressed by existing cryptographic techniques alone, which are often too heavyweight to manage the computation involving a large amount of data. As a result, many computing tasks have to be run on individual organizations? internal systems whenever they touch even a very small amount of sensitive information.The research in this project seeks practical solutions to this critical security challenge. The PIs are working on an approach to split a computing job over a hybrid-cloud platform, delegating to a public cloud the computation over public data, while keeping the computation on sensitive data within a private cloud. Specifically, the PIs are developing a privacy-aware MapReduce system, which transparently partitions a computing job and schedules its components across the public/private clouds according to the security levels of the data involved. The system is designed to achieve high security assurance and outsource most of its workload when possible, at small computational and communication overheads. It includes support for analyzing and transforming the code for legacy jobs as well as developing new jobs. We are also working to extend these techniques to facilitate other secure work-flow processing over hybrid clouds. This research involves industry collaborators and contributes to secure processing of a wide range of computing jobs, from commercial data analysis, to DNA analysis, to intrusion detection.

将数据密集型计算转移到低成本公共云的持续努力一直受到隐私问题的阻碍，因为今天的云提供商对保护敏感用户数据几乎没有保证。现有的加密技术无法单独解决这个问题，因为它们通常过于重量级，无法管理涉及大量数据的计算。因此，许多计算任务必须在单独的组织中运行。内部系统，只要他们接触到哪怕是非常少量的敏感信息。该项目的研究旨在寻求解决这一关键安全挑战的实际解决方案。pi正在研究一种方法，在混合云平台上拆分计算任务，将公共数据的计算委托给公共云，同时将敏感数据的计算保留在私有云中。具体来说，pi正在开发一个隐私感知的MapReduce系统，该系统可以透明地划分计算作业，并根据所涉及数据的安全级别跨公共/私有云调度其组件。该系统旨在实现高安全性保证，并在可能的情况下以较小的计算和通信开销外包其大部分工作量。它包括对分析和转换遗留作业的代码以及开发新作业的支持。我们还在努力扩展这些技术，以促进混合云上的其他安全工作流处理。这项研究涉及行业合作者，有助于安全处理广泛的计算工作，从商业数据分析到DNA分析，再到入侵检测。