Secure multiparty computation (MPC) techniques enable multiple parties to compute joint functions over their private data without sharing that data with 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. Prior policy-agnostic MPC languages relied on 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 into 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 policies.

翻译：安全多方计算（MPC）技术使多个参与方能够在不向其他方共享其私有数据的情况下，联合计算其私有数据上的联合函数，通常通过采用强大的密码协议来保护个人数据。编写此类函数时的一个挑战是，大多数MPC语言强迫用户在单个应用程序中混合程序逻辑与隐私关注点，这使得更改或审计程序的底层隐私策略变得困难。先前的策略无关MPC语言依赖动态强制来将隐私需求与程序逻辑解耦。遗憾的是，由此产生的开销使得操作结构化数据的MPC应用难以扩展。本研究提出通过将程序转换为语义等价的版本，以静态强制用户提供的隐私策略来消除这种开销。我们已在一门名为Taypsi的新型MPC语言中实现了该方法；实验评估表明，所提出的系统在处理涉及结构化数据和复杂隐私策略的多种MPC应用中，展现出显著的性能提升。