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.

隐私问题阻碍了将数据密集型计算转移到低成本公共云的持续努力，因为当今的云提供商几乎没有保证保护敏感用户数据。 仅凭现有的加密技术无法解决这个问题，这些技术通常太重了，无法管理涉及大量数据的计算。结果，许多组织必须执行许多计算任务？内部系统每当触摸少量敏感信息时。该项目的研究都会为这种关键的安全挑战寻求实用的解决方案。 PI正在采用一种方法，将计算作业分开，将计算作业划分在混合云平台上，将计算委托在公共数据上委托计算，同时将计算在私有云中保持敏感数据。 具体而言，PI正在开发一个隐私意识的MapReduce系统，该系统可以根据所涉及的数据的安全级别透明地分配计算作业并在公共/私有云中安排其组件。该系统旨在在小型计算和通信开销中获得高度安全保证和外包大部分工作量。它包括支持分析和转换传统工作的代码以及开发新工作的支持。 我们还努力扩展这些技术，以促进混合云上其他安全的工作流程处理。 这项研究涉及行业合作者，并有助于确保从商业数据分析，DNA分析到入侵检测的广泛计算工作的处理。