CAREER: Distributed Differential Privacy via Secure Multiparty Computation

收集和分析个人资料的速度越来越快，引发了新的隐私和安全问题。差分隐私是一个很有前途的保护个人隐私的框架，但在实践中部署它仍然是一个挑战。差异隐私系统中的错误很难发现，并可能导致意外的隐私故障。此外，这类系统通常需要在中央服务器上收集敏感数据；对这些服务器的攻击可能会导致灾难性的隐私损失。该项目旨在开发应对这两个挑战的工具。该项目的新颖之处在于：(a)验证程序正确实现差异隐私的新技术，以及(b)加密技术的新应用，以保护处理过程中数据的安全性。该项目更广泛的意义和重要性在于，它有可能为数据处理系统中的个人提供更广泛的正确、安全的正式隐私保障实现。该项目的中心目标是能够构建正确的、可扩展的系统，以满足不同的隐私，而不需要可信的数据管理员。为此，该项目旨在设计新的安全协议和新技术，以确保使用这些协议构建的系统的正确性。该项目的具体研究目标包括：(1)设计新的安全协议，利用差异隐私属性来提高性能并扩展到数百万参与者；(2)大规模评估这些协议的新工具；(3)新的自动化程序分析，用于验证安全协议的正确性；(4)新的程序分析和自动化测试方法，用于检查建立在安全协议上的差异私有系统的正确性。这个项目包括教育材料的开发，包括适合安全计算本科课程的面向编程的教科书。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。

The increasing rate of collection and analysis of personal data has led to new privacy and security concerns. Differential privacy is a promising framework for protecting individual privacy, but deploying it in practice remains a challenge. Bugs in differential privacy systems are difficult to find, and can result in unexpected privacy failures. In addition, such systems often require collecting sensitive data on central servers; a compromise of these servers could result in a catastrophic loss of privacy. This project aims to develop tools for addressing both challenges. The project's novelties are: (a) new techniques for verifying that programs correctly implement differential privacy, and (b) new applications of cryptography to protect the security of data during processing. The project's broader significance and importance lies in its potential to enable the broader deployment of correct, secure implementations of formal privacy guarantees for individuals in data processing systems.The central goal of this project is to enable the construction of correct, scalable systems that satisfy differential privacy without the need for a trusted data curator. To this end, the project aims to design both new secure protocols and new techniques for ensuring the correctness of systems built with those protocols. Specific research goals of the project include (1) the design of new secure protocols that leverage properties of differential privacy to increase performance and scale to millions of participants; (2) new tools for evaluating these protocols at scale; (3) new automated program analyses for verifying the correctness of secure protocols; and (4) new program analyses and automated testing approaches for checking the correctness of differentially private systems built on secure protocols. This project includes the development of educational materials, including a programming-oriented textbook suitable for an undergraduate course on secure computation.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.