SaTC: TTP: Small: Experimental Platform for Rapid Prototyping and Deployment of Secure Multi-Party Protocols

这个过渡到实践的项目的目标是为安全多方计算应用的原型和部署建立一个实验平台。安全多方计算是一个概念，它允许对分布在不同数据源的私有数据进行计算，方法是在整个计算过程中通过加密手段保护数据，从而在不学习数据的情况下执行计算任务。过去已经开发了各种不同的技术和工具，用于使用私有数据进行安全计算，但由于技术的复杂性、组织中缺乏足够的专业知识以及现有工具的可用性较差，它们的部署仍然缓慢。该项目有助于更轻松地开发和部署由应用程序设计人员开发的安全多方计算应用程序。这一目标是通过改进和扩展名为Picco的通用安全多方计算编译器来实现的。生成的平台可供非领域专家的研究人员和程序员使用。开发的平台的主要特征之一是其支持的计算的通用性。即，将以常规编程语言的变体编写的程序转换为相应的安全多方计算协议和附加支持程序的实现。该平台是可扩展和可配置的，以满足特定应用及其计算设置的需要。一种新颖的组件包括方便的基于网络的用户界面，用于在程序编译时产生的私有数据输入和输出恢复。这项工作的一部分还包括开发教育材料，以促进在课程和教育项目中使用安全计算，并帮助当地非营利组织建立和运行隐私保护数据分析。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this transition-to-practice project is to build an experimental platform for prototyping and deployment of secure multi-party computation applications. Secure multi-party computation is a concept that permits computing on private data distributed across different data sources by protecting the data via cryptographic means throughout the computation and thus conducting the computational task without learning the data. A variety of different techniques and tools have been developed in the past for secure computation with private data, but their deployment remains slow due to complexity of the techniques, a lack of sufficient expertise in organizations, and poor usability of the available tools. This project facilitates easier development and deployment of secure multi-party computation applications developed by application designers. This goal is accomplished by improving and extending a general-purpose secure multi-party computation compiler called PICCO. The resulting platform can be used by both researchers and programmers who are not domain experts.One of the primary features of the developed platform is the generality of the computation it supports. Namely, a program written in a variant of a conventional programming language is transformed into an implementation of the corresponding secure multi-party computation protocol and additional supporting programs. The platform is extensible and configurable to the needs of specific applications and their computational setup. A novel component includes a convenient web-based user interface for private data entry and output recovery generated at the time of program compilation. A part of the effort also consists of developing educational materials for facilitating the use of secure computation in courses and educational programs and helping local non-profit organizations set up and run privacy-preserving data analysis.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.