Secure multiparty computation (MPC) techniques enable multiple parties to compute joint functions over their private data without sharing that data to other parties, typically by employing powerful cryptographic protocols to protect individual's data. One challenge when writing such functions is that most MPC languages force users to intermix programmatic and privacy concerns in a single application, making it difficult to change or audit a program's underlying privacy policy. Existing policy-agnostic MPC languages rely on run-time / dynamic enforcement to decouple privacy requirements from program logic. Unfortunately, the resulting overhead makes it difficult to scale MPC applications that manipulate structured data. This work proposes to eliminate this overhead by instead transforming programs to semantically equivalent versions that statically enforce user-provided privacy policies. We have implemented this approach in a new MPC language, called Taypsi; our experimental evaluation demonstrates that the resulting system features considerable performance improvements on a variety of MPC applications involving structured data and complex privacy polices.

翻译：安全多方计算（MPC）技术使多方能够在不向其他方共享数据的情况下，对其私有数据执行联合函数计算，这通常通过采用强大的密码学协议来保护个体数据。编写此类函数时面临的挑战之一是，大多数MPC语言迫使用户在单个应用程序中混合编程逻辑与隐私关注点，这使得难以修改或审计程序底层的隐私策略。现有的策略无关MPC语言依赖运行时/动态强制机制来解耦隐私需求与程序逻辑。然而，由此产生的开销使得处理结构化数据的MPC应用难以扩展。本文提出通过将程序转换为语义等价的版本（该版本静态强制用户提供的隐私策略），从而消除这一开销。我们已在名为Taypsi的新型MPC语言中实现了该方法；实验评估表明，该方案在处理结构化数据与复杂隐私策略的多种MPC应用中，实现了显著的性能提升。