Collaborative Research: CICI: Secure and Resilient Architecture: Data Integrity Assurance and Privacy Protection Solutions for Secure Interoperability of Cloud Resources

云计算为用户提供了许多明显的好处，包括可伸缩性和降低系统获取成本。然而，当科研人员从云端访问数据进行实验或分析时，数据的安全性、完整性和隐私性正成为他们关注的主要问题。此外，数据所有者可能不希望向云服务提供商透露其数据，原因可能是数据的敏感性（例如，医疗记录），也可能是因为数据的价值。因此，创建云数据完整性保障和隐私保护解决方案，帮助用户充分拥抱云服务，并保护网络基础设施资源非常重要。使用云数据库，数据所有者可以存储大量的数据。缩放从不同来源收集的数据集。然后，用户可以发起查询，检索数据记录，以便进行研究和实验。然而，有几个可能对查询结果的准确性造成威胁。例如，云数据库可能被破坏，存储的数据可能被篡改。云服务器可能出现故障，导致云数据库无意中返回不完整的查询结果。客户机不太可能知道这种不正确或不完整的查询结果。因此，错误的数据可能被用于随后的科学实验或分析，这可能导致错误的结果。云数据库查询完整性保证是一个关键问题，它支撑着一个安全可信的终端。结束科学的工作流程。这项工作在隐私&amp；#8208；友好的方式，建立在加密数据上的加密查询之上。这是实现数据隐私和数据完整性的关键。数据来源——数据的历史及其处理方式——也是科学工作流程的一个重要方面。然而，确保来源以提供完整性、隐私性和保密性保证也是具有挑战性的，这使得许多科学工作流程难以提供科学数据和查询结果的可验证的来源历史。有了云，提供这样的保证对于数据和来源都是困难的。该项目为在云中存储和计算的所有数据和结果的来源信息的安全收集、存储、传输和验证提供基础设施支持。这种可验证的来源的可用性为科学工作流程提供了好处，通过可验证的历史和结果使过程更加可信。研究团队创建了一个查询完整性保证、数据隐私保护和可验证的来源框架，为支持安全云服务提供了一系列解决方案。该项目通过试点实现以下目标的新型云数据安全方法，为网络安全研究界做出贡献：(1)开发Voronoi图&amp；#8208；基于完整性保证技术，(2)设计云数据库数据隐私保护方法；(3)建模查询完整性保证和查询评估成本之间的权衡；(4)实现安全的云数据来源机制；(5)实现一个原型系统，将所有组件集成在一起进行安全性和性能评估。

Cloud computing provides many clear benefits for users, including scalability and reduced system acquisition cost. However, data security, integrity and privacy are becoming major concerns for scientific researchers when they access data from the cloud to conduct experiments or analytics. In addition, data owners may not want to reveal their data to cloud service providers either because of the sensitivity of the data (e.g., medical records) or because of its value. Therefore, it is important to create cloud data integrity assurance and privacy protection solutions that help users fully embrace cloud services as well as protect cyberinfrastructure resources. With a cloud database, data owners can store large‐scale datasets collected from various sources. Users can then launch queries retrieving the data records for conducting research and experiments. However, there are several possible threats to query result accuracy. For example, a cloud database could be compromised and the stored data could be tampered with. There could be a malfunction in the cloud server, so that the cloud database inadvertently returns incomplete query results. It is unlikely that the client would be aware of such incorrect or incomplete query results. Consequently, erroneous data could be employed in subsequent scientific experiments or analyses, which could lead to false results. Cloud database query integrity assurance is critical issue that underpins a secure and trustworthy end‐to‐end scientific workflow. This work approaches these problems in a privacy‐friendly manner, building on top of encrypted queries over encrypted data. This is key for achieving both data privacy and data integrity. Data provenance - the history of the data and how its been handled - is also an important aspect of scientific workflows. However, securing the provenance to provide integrity, privacy, and confidentiality guarantees is also challenging, making it hard for many scientific workflows to provide a verifiable provenance history of scientific data and query results. With clouds, providing such guarantees is difficult for both data and provenance. This project enables infrastructural support for secure collection, storage, transmission, and verification of provenance information for all data and results stored and computed in the cloud. The availability of such verifiable provenance offers benefits to scientific workflows, making the process more trustworthy via verifiable history and results. The research team creates a query integrity assurance, data privacy protection, and verifiable provenance framework which provides an array of solutions for supporting secure cloud services. This project contributes to the cybersecurity research community by piloting novel cloud data security approaches that accomplish the following goals: (1) developing Voronoi diagram‐based integrity assurance techniques, (2) designing cloud database data privacy protection methods, (3) modeling the trade off between query integrity assurance and query evaluation costs, (4) realizing secure cloud data provenance mechanisms, and (5) implementing a prototype system, where all the components are integrated for security and performance evaluation.