CAREER: Distributed Differential Privacy via Secure Multiparty Computation

收集和分析个人数据的速度越来越快，导致了新的隐私和安全问题。差异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。差异隐私系统中的漏洞很难发现，并且可能导致意外的隐私失败。此外，此类系统通常需要在中央服务器上收集敏感数据;这些服务器的泄露可能导致隐私的灾难性损失。该项目旨在开发应对这两项挑战的工具。该项目的创新之处是：（a）用于验证程序正确实现差分隐私的新技术，以及（B）用于在处理期间保护数据安全的密码学的新应用。该项目的更广泛的意义和重要性在于它的潜力，使更广泛的部署正确的，安全的实现正式的隐私保障个人在数据处理systems.The项目的中心目标是使正确的，可扩展的系统，满足差异隐私的建设，而不需要一个值得信赖的数据管理员。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，利用差分隐私的特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）用于验证安全协议正确性的新的自动程序分析;（4）新的程序分析和自动测试方法，用于检查建立在安全协议上的差分私有系统的正确性。该项目包括教材的开发，包括适合本科生课程的面向编程的安全计算教科书。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。

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.