Secure and Privacy Preserving Big Data Analytics Curriculum Development

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。大数据上的计算可能需要大量的计算资源，虽然一个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节省成本。在这些情况下，数据可能会被转移到第三方服务，从而引起与第三方计算设备的可信度和敏感信息的安全性相关的担忧。当使用第三方服务器进行计算时，数据本质上在不受信任的环境中变得可用，例如：在数据传输期间对黑客可用，或者对执行计算的第三方位置的攻击者可用。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进行加密保证。德克萨斯大学达拉斯分校（UT Dallas）提出的项目将通过开发大数据安全和隐私（BDSP）的实验教育计划来解决这一需求。该项目将在BDSP中开发一套实验室练习和课程模块，并将与学术界、政府和工业界共享。支持可信执行环境（TEE）的嵌入式硬件技术的最新进展为云计算、可信数据分析和应用加密领域的研究带来了令人兴奋的机会。通过在计算的安全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。有必要为数据分析模型建立一个科学的基础，以利用tee、评估它们的局限性、探索性能开销并分析诸如侧通道之类的安全含义。本计划将建立一个BDSP实验室，供学生进行可信分析的实验。研究结果将被纳入达拉斯德州大学现有的几门网络安全课程。此外，该项目将产生一个顶点课程在BDSP。目标是在BDSP中培养一支熟练的劳动力队伍，以安全地分析用于为政府、工业和学术界提供更好服务的大量数据。此外，与德克萨斯大学达拉斯分校已成立的女性参与网络安全中心合作，该项目将鼓励、激励女性和少数族裔学生参与网络安全，并为她们提供机会。该项目将利用该中心招募高素质的女性和少数民族学生进入BDSP的教育项目。

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.