CAREER: Verifiable Outsourcing of Data Mining Computations

职业：数据挖掘计算的可验证外包

• 批准号: 1350324
• 负责人: Wendy Hui Wang
• 金额: $ 47.12万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1350324&HistoricalAwards=false
• 关键词: CAREER; Verifiable; Outsourcing; Data; Mining

Spurred by developments such as cloud computing, there has been considerable interest in the data-mining-as-a-service (DMaS) paradigm in which a client outsources his/her data mining needs to a third-party service provider. However, this raises a few security concerns. One of the security concerns is that the service provider may return plausible but incorrect mining results to the client. There is a crucial need for techniques that enable the client to verify, without much effort, that the service provider has performed the outsourced computations faithfully and returned correct mining results. Despite the recent intensive efforts on verifiable general-purpose computations, efficient result verification of data mining computations remains a largely unexplored territory. This CAREER proposal aims at designing efficient and practical verification techniques for data mining computations outsourced to an untrusted service provider. Research activities include developing (1) innovative verification approaches for data mining computations without any privacy preservation mechanisms; (2) new verification approaches for privacy-preserving data mining computations; (3) novel methods for the analysis of attack types and modeling of the collusion behaviors of service providers; and (4) a full system approach in developing, deploying, and evaluating the proposed techniques.Advances in verifiable outsourcing of data mining computations can spur wider adoption of cloud services. This project also includes curriculum development and the training of high school, undergraduate, graduate, women and students in underrepresented groups.

在云计算等发展的推动下，人们对数据挖掘即服务（data-mining-as-a-service, DMaS）范式产生了相当大的兴趣，在这种范式中，客户将其数据挖掘需求外包给第三方服务提供商。然而，这引起了一些安全问题。安全问题之一是服务提供者可能向客户端返回可信但不正确的挖掘结果。非常需要一种技术，使客户能够不费太多力气地验证服务提供商是否忠实地执行了外包计算并返回了正确的挖掘结果。尽管最近对可验证的通用计算进行了大量的努力，但数据挖掘计算的有效结果验证仍然是一个很大程度上未开发的领域。本CAREER提案旨在为外包给不受信任的服务提供商的数据挖掘计算设计有效和实用的验证技术。研究活动包括开发(1)无任何隐私保护机制的数据挖掘计算的创新验证方法；(2)保护隐私数据挖掘计算的新验证方法；(3)服务提供商合谋行为的攻击类型分析和建模新方法；(4)开发、部署和评估所建议技术的完整系统方法。可验证的数据挖掘计算外包方面的进步可以刺激云服务的更广泛采用。该项目还包括课程编制和对高中、本科生、研究生、妇女和代表性不足群体学生的培训。