CICI:UCSS: ARMOR: Secure Querying of Massive Scientific Datasets

目前，科学数据通常对正在进行的研究合作很重要，但在云或服务器中共享，几乎没有任何保护。这项工作的重点是解决此类敏感数据的安全和隐私风险的技术解决方案。所选择的特定类型的解决方案方法基于一类称为加密数据查询的技术。这些方法涉及一种特定类型的加密，允许（云）服务器回答客户端提出的查询，同时保持数据加密。与其他方法相比，这些解决方案具有较低的开销，但信息泄漏到服务器的风险较高。解决方案及其局限性都取决于数据和查询的性质，以前的工作并没有明确地集中在复杂的科学数据和相关的查询。通过这种观察，这项工作旨在将加密数据查询建立为保护云上敏感科学数据的有吸引力的方法。科学数据的特点和查询的典型性决定了加密数据查询的现有技术在方法本身、泄漏攻击的设计和泄漏风险的理解等方面需要进一步扩展。研究重点包括：1）新的加密和查询处理技术，其中解决了几种类型的查询的挑战，包括基于等式的选择，多维范围选择以及基于值相似性或范围的连接。2)面向规模和效率的设计，包括支持连接和加密的新颖表示，以及科学数据处理中的并行性如何与加密相结合。3)评估科学背景下的间接费用和泄漏，其中使用几个真实的案例研究间接费用和泄漏。 在这个过程中，最近关于泄漏攻击的文献正在扩展到科学案例和正在开发的方法。 就更广泛的影响而言，由此产生的工作将直接导致云中和组织服务器上敏感科学数据的更高安全性。这一努力还包括对课程和人力资源开发的许多贡献。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Currently, scientific data, which is often important for ongoing research collaborations, is shared in clouds or servers with almost no protection. This effort focuses on technological solutions to address such sensitive data's security and privacy risks. The specific type of solution approach chosen is based on a class of techniques known as encrypted data querying. These methods involve a particular type of encryption that allows the (cloud) servers to answer queries asked by clients while keeping the data encrypted. Compared to other approaches, these solutions have lower overheads but a higher risk of information leakage to the server. Both the solutions and their limitations depend upon the nature of the data and the queries, and previous work has not explicitly focused on complex scientific data and associated queries. With this observation, this effort aims to establish encrypted data querying as an attractive method for protecting sensitive scientific data on clouds. A highly interdisciplinary team with expertise in system security, cryptography, scientific data management, and data science workflows supports the agenda of this work.With distinct characteristics of science data and typical queries on them, the current state-of-the-art in encrypted data querying needs to be extended in terms of the methods themselves, the design of leakage attacks, and the understanding of leakage risks. The research thrusts include: 1) New Encryption and Query Processing Techniques, where challenges with several types of queries that include equality-based selection, multi-dimensional range selection, and joining on value similarity or ranges are addressed. 2) Scale and Efficiency Oriented Designs, which includes novel representation that supports joins and encryption, and how parallelism in scientific data processing can be combined with encryption. 3) Evaluating Overheads and Leakage in Science Contexts, where the overheads and leakage are studied using several real cases. In the process, the recent literature on leakage attacks is being extended to work with science cases and the methods being developed. In terms of broader impact, the resulting work will directly result in higher security of sensitive science data in the cloud and on organizational servers. This effort also involves many contributions to curriculum and human resource development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.