Secure and Privacy Preserving Big Data Analytics Curriculum Development

安全和隐私保护大数据分析课程开发

• 批准号: 1723602
• 负责人: Bhavani Thuraisingham
• 金额: $ 50万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1723602&HistoricalAwards=false
• 关键词: Secure; Privacy; Preserving; Big; Data

Organizations own and generate massive amounts of data that can be analyzed to generate understanding on customers as well as optimize consumer services. Computation on Big Data may require massive computational resources and, while an organization may have the capability to carry-out these analyses, it may use a third-party service to outsource some computations to be cost-effective. In these cases, data may be transferred to a third-party service, giving rise to concerns associated with the trustworthiness of the third-party computational devices and the security of sensitive information. When a third-party server is used for computation, data inherently becomes available in untrusted environments, for example: to a hacker during data transmission, or to adversaries at the third-party location where the computation is performed. Data owners may need to protect their data, and require cryptographic guarantees about data security and integrity of computational output from these third-party services. The proposed project from the University of Texas at Dallas (UT Dallas) will address this need by developing an experimental education program for Big Data Security and Privacy (BDSP). The project will develop a set of laboratory exercises and course modules in BDSP that will be shared with academia, government and industry.Recent advancements in embedded hardware technology to support trusted execution environment (TEE) have generated exciting opportunities for research in the field of cloud computing, trusted data analytics, and applied cryptography. By protecting code and data within a secure region of computation, a cloud service can ensure confidentiality and integrity of data and computations. There is a need to establish a scientific foundation for data analytical models to leverage TEEs, evaluate their limitations, explore performance overhead, and analyze security implications such as side channels. The proposed project will generate a BDSP laboratory for students to carry out experimentation in trustworthy analytics. The results will be incorporated into several of the existing courses in Cyber Security at UT Dallas. In addition, the project will generate a capstone course in BDSP. The goal is to develop a skilled workforce in BDSP to securely analyze the vast amounts of data that are used to provide better services in government, industry and academia. Further, with the established Center for Engaging Women in Cyber Security at The University of Texas at Dallas, this project will encourage, motivate and provide opportunities for female and minority students in cyber security. The project will leverage the Center to recruit highly qualified female and minority students into the education program in BDSP.

组织拥有并生成大量数据，可以对这些数据进行分析，以了解客户并优化客户服务。大数据计算可能需要大量计算资源，虽然组织可能有能力执行这些分析，但它可能会使用第三方服务来外包一些计算，以实现成本效益。在这些情况下，数据可能会被传输到第三方服务，从而引起与第三方计算设备的可信性和敏感信息的安全性相关的担忧。当第三方服务器用于计算时，数据在不受信任的环境中自然变得可用，例如：在数据传输期间对黑客可用，或对执行计算的第三方位置的对手可用。数据所有者可能需要保护他们的数据，并要求对这些第三方服务的数据安全性和计算输出的完整性提供加密保证。德克萨斯大学达拉斯分校(UT Dallas)提出的这一项目将通过开发大数据安全和隐私(BDSP)实验教育计划来满足这一需求。该项目将开发一套BDSP的实验室练习和课程模块，并将与学术界、政府和行业共享。支持可信执行环境(TEE)的嵌入式硬件技术最近的进步为云计算、可信数据分析和应用密码学领域的研究创造了令人兴奋的机会。通过在安全的计算区域内保护代码和数据，云服务可以确保数据和计算的机密性和完整性。需要为数据分析模型建立科学基础，以利用TEE、评估其限制、探索性能开销并分析安全影响，例如侧通道。拟议的项目将产生一个BDSP实验室，供学生在可信分析方面进行实验。研究结果将被纳入德克萨斯大学达拉斯分校现有的网络安全课程。此外，该项目将在BDSP中产生一个顶峰课程。目标是在BDSP中培养一支熟练的员工队伍，以安全地分析用于为政府、行业和学术界提供更好服务的海量数据。此外，随着德克萨斯大学达拉斯分校建立了妇女参与网络安全中心，该项目将鼓励、激励和为女性和少数族裔学生提供网络安全方面的机会。该项目将利用该中心招收高素质的女性和少数族裔学生参加BDSP的教育计划。