TWC: Medium: Collaborative: New Protocols and Systems for RAM-Based Secure Computation

TWC：媒介：协作：基于 RAM 的安全计算的新协议和系统

• 批准号: 1564088
• 负责人: Samuel Gordon
• 金额: $ 37.1万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-15 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564088&HistoricalAwards=false
• 关键词: TWC; Medium; Collaborative; New; Protocols

Secure computation allows users to collaboratively compute any program on their private data, while ensuring that they learn nothing beyond the output of the computation. Existing protocols for secure computation primarily rely on a boolean-circuit representation for the program being evaluated, which can be highly inefficient. This project focuses on developing secure-computation protocols in the RAM model of computation. Particularly challenging here is the need to ensure that memory accesses are oblivious, and do not leak information about private data. The investigators are designing efficient oblivious data structures that can be used as general-purpose building blocks for secure protocols in the RAM model of computation. This project develops a framework that enables programmers to write high-level code that can then be compiled by a back-end algorithm that analyzes the code and makes use of the oblivious data structures we provide. This work is influenced by the needs of real applications, and the techniques to analyze the exact requirements of the program will evaluated and to tailor the resulting protocol to those requirements. This project aims to develop tools making secure computation more accessible to non-specialists, so it can be more broadly used to perform computations on private data. The Principal Investigators on this project mentor both graduate and undergraduate students and actively encourage involvement of minority students. The project develops new course materials and interact with the broader community through involvement in the IEEE cybersecurity initiative and the Maryland Cybersecurity Council.

安全计算允许用户在他们的私人数据上协作计算任何程序，同时确保他们除了计算输出之外什么也学不到。现有的安全计算协议主要依赖于被评估程序的布尔电路表示，这可能是非常低效的。该项目专注于在计算的RAM模型中开发安全计算协议。这里特别具有挑战性的是，需要确保内存访问是可以忽略的，并且不会泄露有关私有数据的信息。研究人员正在设计高效的不经意数据结构，这些结构可以用作RAM计算模型中安全协议的通用构建块。这个项目开发了一个框架，使程序员能够编写高级代码，然后可以通过后端算法进行编译，该算法分析代码并利用我们提供的不经意的数据结构。这项工作受到实际应用程序需求的影响，将评估分析程序确切需求的技术，并根据这些需求定制结果协议。该项目旨在开发工具，使非专业人员更容易进行安全计算，从而可以更广泛地用于对私人数据进行计算。该项目的首席调查员指导研究生和本科生，并积极鼓励少数族裔学生参与。该项目开发新的课程材料，并通过参与IEEE网络安全倡议和马里兰州网络安全理事会与更广泛的社区互动。