SaTC: CORE: Small: Program Analysis and Transformations for Secure Computation on the Cloud

SaTC：核心：小型：云上安全计算的程序分析和转换

• 批准号: 1814898
• 负责人: Ana Milanova
• 金额: $ 48.3万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1814898&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Program; Analysis

Cloud service providers offer a wide range of data storage and computation products. Customers increasingly outsource data and computation to third-party cloud providers. However, when customers upload their data to the cloud provider, they relinquish data confidentiality. As countless recent events attest, securing data and computation is a problem of tremendous importance. This project explores whether customers can take advantage of inexpensive, efficient, and convenient cloud services, including computation, while preserving to some extent data confidentiality, and retaining efficiency. This project builds new program analyses that mitigate the impact of data conversion, and transform programs to compute securely using these methods. This research leverages prior work on SecureMR, a system that automatically transforms a MapReduce program into one that works over encrypted data on a public cloud. This project addresses a key issue with SecureMR, namely, that a single trusted client is bound to limit scalability of highly parallel MapReduce programs. This project envisions program analyses and transformations that split work between two cloud systems, and explores key challenges in building models and analyses that guarantee confidentiality of data such that neither system can reconstruct the original plaintext input data. The project develops SecureMR-2PC, based on 2-party computation (2PC), which splits input data into shares among two clouds, to avoid leakage of aggregate data and achieve provable privacy guarantees. Additionally, the project develops novel program analysis techniques that reason about sharing protocols to improve efficiency of multi-party computation. The project builds a comprehensive benchmark suite of MapReduce programs to facilitate research on secure computation on the cloud.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.

云服务提供商提供广泛的数据存储和计算产品。客户越来越多地将数据和计算外包给第三方云提供商。但是，当客户将数据上传到云提供商时，他们放弃了数据的机密性。正如最近无数事件所证明的那样，保护数据和计算是一个非常重要的问题。该项目探讨客户是否可以利用廉价，高效和方便的云服务，包括计算，同时在一定程度上保持数据机密性并保持效率。该项目构建了新的程序分析，以减轻数据转换的影响，并使用这些方法将程序转换为安全的计算。这项研究利用了SecureMR之前的工作，SecureMR是一个自动将MapReduce程序转换为在公共云上处理加密数据的系统。该项目解决了SecureMR的一个关键问题，即单个可信客户端必然会限制高度并行的MapReduce程序的可扩展性。该项目设想了在两个云系统之间分配工作的程序分析和转换，并探讨了构建模型和分析的关键挑战，以保证数据的机密性，使两个系统都无法重建原始的明文输入数据。该项目开发了基于两方计算（2 PC）的SecureMR-2 PC，将输入数据分割为两个云之间的份额，以避免聚合数据的泄漏并实现可证明的隐私保证。此外，该项目开发了新的程序分析技术，原因共享协议，以提高多方计算的效率。该项目建立了一个全面的MapReduce程序基准套件，以促进云安全计算的研究。该奖项反映了NSF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。