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

目前，通常对正在进行的研究合作很重要的科学数据是在几乎没有任何保护的云或服务器上共享的。这项工作的重点是解决这些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是基于一类称为加密数据查询的技术。这些方法涉及一种特殊类型的加密，允许（云）服务器在保持数据加密的同时回答客户端请求的查询。与其他方法相比，这些解决方案的开销较低，但信息泄露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关注复杂的科学数据和相关查询。通过这种观察，这项工作旨在建立加密数据查询，作为保护云上敏感科学数据的一种有吸引力的方法。一个具有系统安全、密码学、科学数据管理和数据科学工作流程专业知识的高度跨学科团队支持这项工作的议程。由于科学数据具有鲜明的特点和典型的查询方式，目前的加密数据查询技术需要在方法本身、泄漏攻击的设计以及对泄漏风险的认识等方面进行扩展。研究重点包括：1)新的加密和查询处理技术，其中解决了几种查询类型的挑战，包括基于平等的选择、多维范围选择和基于值相似或范围的连接。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，以及如何将科学数据处理中的并行性与加密相结合。3)评估科学背景下的管理费用和泄漏，其中使用几个真实案例研究管理费用和泄漏。在此过程中，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和组织服务器上敏感科学数据的更高安全性。这项工作还涉及对课程和人力资源开发的许多贡献。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.